Unni Cathrine Eiken February 2005

University of Bergen
Section for linguistic studies
CORPUS-BASED
SEMANTIC CATEGORISATION
FOR ANAPHORA RESOLUTION
Unni Cathrine Eiken
Cand. Philol. Thesis in
Computational Linguistics and
Language Technology
February 2005

Abstract
This thesis describes an approach of using corpus-based classification of semantically
related words as a referent-guessing helper in anaphora resolution. A small limiteddomain
corpus was collected and using a method based on semantic structures available
from syntactic parses of the texts, elementary predicate-argument structures were
extracted from it. The extracted structures were processed using an association technique
which created bundles of semantically similar words based on their distribution in the text
collection. The groups of semantically similar words represent valid selectional
restrictions for the domain of the text collection in the sense that they characterise types
of arguments which tend to occur in certain contexts. These groups can be used to create
an expectation of which words to expect in a given contextual pattern, and thus be used in
anaphora resolution to select a probable referent from a set of possible referents. The
experiments in the thesis show that this approach produces promising results; the concept
groups can function as a helper to find likely referents in anaphora resolution.
Sammendrag
Metoden som beskrives i denne hovedoppgaven bygger på korpusbasert klassifikasjon av
semantisk like ord og relaterer dette til bruk innenfor anaforresolusjon. Et
domenespesifikt korpus ble samlet, og forenklede predikat-argumentstrukturer ble
ekstrahert ved hjelp av en metode basert på semantiske strukturer som er tilgjengelige
etter en syntaktisk analyse av tekstene. Strukturene ble prosessert med en
assosiasjonsteknikk som, basert på ordenes distribusjon i tekstsamlingen, dannet
grupperinger av semantisk like ord. Disse ordgruppene representerer gyldige
seleksjonsrestriksjoner innenfor tekstsamlingens avgrensede domene da de karakteriserer
grupper av argumenter som forekommer i gitte kontekster. Ordgruppene kan brukes til å
gi en indikasjon på hvilke ord som forventes i et gitt kontekstmønster. Ved
anaforresolusjon kan dette være til hjelp ved utvelgelsen av en sannsynlig referent fra en
liste med mulige referenter. Eksperimentene i oppgaven viser at denne metoden gir
lovende resultater; ordgruppene kan fungere som et hjelpemiddel i prosessen med å finne
sannsynlige referenter i anaforresolusjon.
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Preface
The project presented in this paper is a Cand. Philol. thesis in Computational Linguistics
and Language Technology and is submitted at the University of Bergen in February 2005.
The thesis is written in loose cooperation with the research project KunDoc (KunDoc
2004). KunDoc (Kunnskapsbasert dokumentanalyse / Knowledge-based document
analysis), which was started in October 2003 and is funded by the Norwegian Research
Council (NFR), has functioned as an inspiration for verbalising the approach in the thesis.
The research within KunDoc is carried out in cooperation between the firm CognIT AS
(CognIT 2004) and the University of Bergen. KunDoc aims at developing a method for
the automatic recognition of discourse structures in written Norwegian texts. The project
examines whether automated identification of coreference in a text can be used to create
an unambiguous discourse structure of the text, identifying both its thematic and
contextual structure. A further goal is to examine whether these techniques are useful
within a closed thematic domain to create unambiguous automated summaries. Within
KunDoc, it is of interest to generate ontologies that represent real-world knowledge.
In the work on my thesis I have also worked in co-operation with the research project
NorGram (NorGram 2004) at the University of Bergen. This project develops a
computational grammar for Norwegian bokmål and is a part of the ParGram project at
Palo Alto Research Center. The pre-processing of the text collection used in my project
has been carried out using NorGram’s grammar on the XLE platform.
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Acknowledgements
I would like to thank my supervisors professor Koenraad de Smedt and professor Helge
Dyvik who have given me invaluable support and new ideas, especially in the process of
developing the method in the thesis.
The approach in the thesis has been developed in loose cooperation with KunDoc. In this
connection I wish to thank Till Christopher Lech at CognIT AS, who has contributed
with tips and support.
Other people I would like to thank are Paul Meurer at Aksis, who installed XLE and
NorGram on my home Linux computer and Martin Rasmussen Lie who has been a great
help in programming questions and has implemented one of the approaches used in the
thesis in Perl. Thanks also go to Aleksander Krzywinski, whose achievement it is that the
pink computer exists.
Many people who have been a great support in the process of finishing the thesis have not
been mentioned – they are still, however, very greatly thanked. You know who you are!
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Table of Contents
1 INTRODUCTION AND PROBLEM STATEMENT 1
1.1 Project outline 3
2 THEORETICAL BACKGROUND 6
2.1 Anaphora resolution 6
2.1.1 Frameworks for anaphora resolution 8
2.1.2 Computational approaches to anaphora resolution 12
2.1.3 Anaphora resolution and text summarisation 22
2.2 Finding meaning in the context 24
2.2.1 The distributional approach 24
2.2.2 Different types of context 27
2.2.3 Context and selectional restrictions 30
3 FROM TEXT TO EPAS – THE EXTRACTION METHOD 33
3.1 Selecting the texts 33
3.2 Predicate-argument structures 35
3.2.1 What is represented in the EPAS? 39
3.3 Parsing with NorGram 42
3.3.1 NorGram in outline 43
3.3.2 Extracting EPAS from NorGram 44
3.4 Altering the source 47
3.5 Finding the words 48
3.6 Evaluation of the data set 52
3.6.1 Errors from the grammar 52
3.6.2 Irrelevant structures 53
3.6.3 Manually added structures 54
3.6.4 Comments about the EPAS list 55
4 CLASSIFICATION 58
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4.1 Step I: Classification with TiMBL 59
4.1.1 The Nearest Neighbor approach 60
4.1.2 Testing 61
4.1.3 Comments on the results 68
4.2 Step II: Association of concept groups 68
4.2.1 Classify 72
4.2.2 Associated concept classes 73
4.3 Step III: Using concept groups in TiMBL 74
4.3.1 Testing 75
4.4 Are concept classes useful for anaphora resolution? 78
5 FINAL REMARKS 82
5.1 Is a parser vital for the extraction process? 82
5.2 Summary and conclusions 84
6 REFERENCES 86
APPENDIX A: EKSTRAKTOR.PL – ALGORITHM 89
APPENDIX B: EKSTRAKTOR.PL – PROGRAM CODE 92
APPENDIX C: THE EPAS LIST 101
APPENDIX D: TEXT ALIGNED WITH EPAS 105
APPENDIX E: CLASSIFY.PL – PROGRAM CODE 110
APPENDIX F: POS-BASED STRUCTURES 113
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1 Introduction and problem statement
For many applications within the field of Natural Language Processing (NLP) it is vital to
identify what a pronoun refers to. Consider a piece of text where (1-1a) is followed immediately
by (1-1b) 1 .
(1- 1)
a.
Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig
kommer til å drepe igjen.
The sergeant leading the investigation says that the perpetrator probably will
kill again.
b. Han etterlyser vitner som var i sentrum søndag kveld.
He puts out a call for witnesses who were in the city centre Sunday evening.
In an application consisting of, for example, summarising the text, a selection of the second
sentence (1-1b) without the preceding sentence (1-1a) leaves the reader with the pronoun han
(he), the referent of which cannot be identified. The task of identifying the referent of a pronoun
is called anaphora resolution and its computer implementation is relevant in many NLP
applications, such as machine translation, automatic abstracting, dialogue systems, question
answering and information extraction.
The problem of correctly identifying the referent of a pronoun is not trivial, as will be apparent
from the comparison of examples (1-1) and (1-2). As will be further described in section 2.1,
strategies that do not incorporate some sort of real-world knowledge cannot confidently identify
the entities that the pronoun han (he) is linked to in these examples.
(1- 2)
Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig
kommer til å drepe igjen. Han ble observert i sentrum søndag kveld.
The sergeant leading the investigation says that the perpetrator probably will
kill again. He was observed in the city centre Sunday evening.
1
The sentences in (1-1) and (1-2) are constructed example sentences and are not part of the data set collected and
used in this thesis.
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This thesis explores the value of using co-occurrence patterns to create concept groups that can
act as an aid in the process of finding what a pronoun refers to. In order to find the entity that the
pronoun han (he) refers to in example (1-1), the following two alternative patterns can be
considered:
(1- 3)
a. lensmann etterlyser vitne sergeant calls-for witness
b. gjerningsmann etterlyser vitne perpetrator calls-for witness
When considering which of these patterns is the most likely one, data collected from a corpus
can be consulted (Dagan and Itai 1990; Dagan et al. 1995; Nasukawa 1994; inter al.). If one of
the patterns is found literally in the corpus, it will receive a strong preference. If none of the
patterns occur in the data collection, similar patterns can be considered. Given that the patterns
in example (1-4) below do feature in the data collection, they can contribute to guessing the
correct referent for the anaphor in example (1-1):
(1- 4)
a. politi etterlyser vitne police call-for witness
b. etterforsker etterlyser vitne investigator calls-for witness
c. lensmann avhører vitne sergeant interviews witness
d. politi avhører person police interview person
e. gjerningsmann dreper offer perpetrator kills victim
f. gjerningsmann angriper kvinne perpetrator attacks woman
In view of the patterns in (1-4), the word lensmann (sergeant) engages in contexts similar to
those of politi (police), which in turn occurs in similar contexts to etterforsker (investigator). By
using association techniques, lensmann can be associated with the other arguments which occur
in similar linguistic environments, and subsequently be preferred as the referent in (1-1).
Approaches within the field of anaphora resolution have in recent years focused on knowledge-
poor strategies used in combination with corpora, at the same time, the notion of constructing a
large and comprehensive base of real-world knowledge has been abandoned somewhat (see
Mitkov 2003 for a brief overview). The approach in the present work expands the co-occurrence
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patterns found in a text collection to also consider semantically similar words and patterns to
those present in the corpus. The association of semantically similar concepts is carried out
through machine learning techniques and an association technique developed for this project.
The project described in the present work develops and examines a method for the automatic
inference of concept groups consisting of semantically similar words from a collection of
limited domain texts. Information that becomes available by automatically classifying context
patterns from a closed thematic domain is examined for the purpose of aiding in anaphora
resolution. The resulting concept groups can function as a form of real-world knowledge by
representing information about which words can be expected in certain contextual environments.
As it is an established problem within the field of computational linguistics that the construction
of knowledge bases requires such a high amount of manual labour, it is of interest to examine
methods which can contribute to automating this task.
1.1 Project outline
This thesis describes a method for the automatic association of clusters of semantically similar
words collected from a limited thematic domain. The association of concepts is based on the
distribution of arguments in particular syntactic contexts.
The method described consists of three steps:
1) the extraction method, which deals with the extraction of meaning structures from a text
corpus
2) the classification method, which deals with the association of the extracted meaning
structures into concept groups
3) the application of the meaning structures and the concept groups to anaphora resolution
In the extraction phase, semantic structures mainly corresponding to subject-verb-object
relations are extracted and normalised to the form shown in (1-5) below. This type of relation is
in this thesis termed an elementary predicate-argument structure (EPAS) and is described in
greater detail in section 3.2.
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(1- 5)
predicate, argument 1, argument 2
In the classification phase, the extracted structures undergo processes that result in the grouping
of concepts into clusters of semantically similar words.
The evaluation of the results obtained by using the method developed in the project is twofold:
• the resulting concept classes are evaluated; does the method produce semantic clusters
that are valid for the thematic domain of the text collection?
• the usefulness of using the concept classes in anaphora resolution is evaluated; does the
method provide a means to infer which entity is referred to in examples such as (1-1)
and (1-2)?
Chapter 3 describes the extraction method, which uses output from a syntactic parser to collect
semantic structures in the form of EPAS from the texts. The text collection used in this project
consists of newspaper texts all concerning a criminal case. The constraints that hold on the
corpus are further described in sections 3.1 and 3.4. Section 3.2 explains the format of the
meaning structures extracted from the text corpus as well as the motivation for choosing EPAS
as meaning representation. In sections 3.3 and 3.5 the process of parsing the texts and gathering
the meaning representations from the parse output is outlined in further detail. Finally, the list of
EPAS resulting from the extraction method is evaluated in section 3.6.
The classification method is described in chapter 4. In section 4.1 a classification approach using
machine learning techniques is described, in section 4.2 the constituents of the EPAS are
associated into semantically similar groups based on their contextual distribution and finally
these two approaches are applied in connection with one another in section 4.3. In section 4.4
the potential of using concept groups in anaphora resolution is discussed.
Final remarks and conclusions are found in section 5. Here the foundation of the extraction
method is also briefly discussed.
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The results obtained in this project provide a preliminary indication of the feasibility and
usefulness of a referent-guessing helper such as described in the introduction. Hindle (1990)
states that small corpora and human intervention in the analysis phase are factors that have
contributed to obscuring the usefulness of semantic classification based on distributional
context. Within the framework of the present work it was not possible to conduct a large-scale
corpus-based study. This is partly due to the lack of a robust and powerful enough extraction
method and will be discussed in greater detail in chapter 3. The text collection that the study is
based on is clearly much too small to offer anything but a tendency and the degree in which the
extraction process is manually manipulated is too high to call the method fully automated.
Nevertheless, this paper describes a pilot study and provides an indication of the quality and
usefulness of the method.
Before going on to describing the method developed in the present work, a brief introduction to
the concepts of context and anaphora resolution is needed. The importance and usefulness of
classifying words according to the contexts they occur in, as well as a brief background on
anaphora resolution, is provided in chapter 2.
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2 Theoretical background
In order to understand the motivation for developing an extraction and classification method as
described in the present work, one needs a brief explanation of the theoretical foundation on
which the method is based. In this chapter, the theoretical background of the method is
described. In section 2.1 the concept of anaphora resolution and the need for context information
in anaphora resolution systems is outlined. In section 2.2 the notion of using context as a means
to identify semantically similar words is explained.
2.1 Anaphora resolution
Most natural language texts contain an abundance of pronouns and other expressions which are
referentially linked to other items in the texts. In order to understand the meaning conveyed by a
text, one needs a method to find out which entities these expressions are linked to. It is difficult
to determine what a pronoun refers to without taking the notion of context and real-world
knowledge into account. Natural language requires a certain amount of context to be intelligible.
We distinguish between linguistic context, which denotes the concrete linguistic setting that a
given word occurs in, and a more general notion of context that refers to the non-linguistic
setting. In the following, a background on the theoretical basics of anaphora will be given,
before some approaches to anaphora resolution are briefly outlined.
Anaphor and referring expression are both terms that are used for words that point back either to
other words or to entities in the world. Anaphora 2 can be defined as the linguistic phenomenon
of using an anaphor to point back to a previously mentioned item in a text (Mitkov 2003, p.
266).
In the Oxford Concise Dictionary of Linguistics (Matthews 1997), a referring expression is
defined as a linguistic element that refers to a specific entity in the real world, termed a referent.
A referring expression can be any natural language expression that is used to refer to a realworld
entity, including nouns and pronouns. As such the linguistic expressions James and he in
a given text may both refer to a person called “James” existing in the real world.
2 The term anaphora is in the present work used in alignment with current literature on anaphora resolution.
Anaphora is the linguistic phenomenon of an anaphor pointing to another item in the text and should not be
understood as the plural form of anaphor, which is anaphors.
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The term anaphor describes a linguistic element, often a pronoun or a nominal, which is linked
to another linguistic element previously presented in the text (Mitkov 2003). An anaphoric
reference is usually supported by a preceding nominal, which is called an antecedent. If a
referring pronoun is mentioned previous to the mentioning of its referent, the term cataphora
applies (Jurafsky and Martin 2000, p. 675). Anaphora provides us with an indirect reference to a
real-world entity. When a referring expression, such as James, has been introduced in a text, it
allows for subsequent reference by anaphors, such as he or the boy. The original referring
expression is therefore the antecedent of the subsequent referring anaphor, for example the
pronoun he. If the anaphor and the antecedent it is linked to both have the same referent in the
real world, they are termed coreferential (Mitkov 2003, p. 267).
(2- 1)
Politimannen sier at han har flere observasjoner
The policeman says that he has several observations
In example (2-1) above, the pronoun han (he) is an anaphor which points back to its antecedent,
the referring expression politimannen (the policeman). Han and politimannen both refer to the
same real-world referent, the object “the policeman”, and are therefore coreferential.
There are various and complex structural conditions on the co-occurrence of an anaphor and its
antecedent. This includes constraints on how far away from each other the antecedent and the
referring anaphor can be without disturbing the understanding of the text. An elaborate
discussion of these conditions is, however, not within the scope of the present work.
Mitkov (2003, p. 268) distinguishes between the following types of anaphora:
• pronominal anaphora: The anaphor is a pronoun.
• lexical noun phrase anaphora: The anaphor is a definite description or proper name that
gives additional information and has a meaning independent of the antecedent.
• verb anaphora: The anaphor is a verb and refers to an action.
• adverb anaphora: The anaphor is an adverb.
• zero anaphora: The anaphor is implicitly present in the text, but physically omitted.
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• nominal anaphora: The anaphor has a non-pronominal noun phrase as antecedent.
o direct: anaphor and antecedent are linked through identity, synonymy,
generalisation or specialisation.
o indirect: anaphor and antecedent are linked through part-of-relations or set
membership.
He states that pronominal anaphora is the most frequent type, while nominal anaphora (indirect
anaphora in particular) usually requires real-world knowledge to be resolved. In this thesis, the
described method will be tested using occurrences of pronominal anaphora from the text
collection.
2.1.1 Frameworks for anaphora resolution
Anaphora resolution is the process of determining the antecedent of an anaphor (Mitkov 2003,
p. 269). In our minds, we build a discourse model that represents the entities mentioned in the
discourse and shows the relationship between them (Webber 1978, in Jurafsky and Martin
2000). A representation is evoked into the model upon the first mentioning in the discourse, and
then subsequently accessed from the model if the entity is mentioned again, either by name or
by way of anaphora. Entities will have varying degrees of salience in the discourse model,
depending on how frequently they have been mentioned and also on how long ago they last were
mentioned. This notion of a discourse model is used both in theories which aim at describing the
process of anaphora resolution, and in computational approaches that automate the anaphora
resolution process.
There are different approaches to resolving referring expressions and anaphors that occur in
natural language discourse. Several formalisms offer frameworks describing the theory of
discourse representation in general and anaphora binding in particular. In the following sections,
two of these formalisms will be briefly outlined.
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2.1.1.1 Discourse representation theory
Discourse representation theory (DRT), proposed by Hans Kamp in 1981, represents a way of
creating dynamic semantic representations for natural language discourse. The framework aims
at representing larger linguistic units than sentences and is particularly useful for representing
the way discourse changes with every new sentence that is introduced. The core structure within
DRT is the discourse representation structure (DRS), which is transformed through the
processing of each sentence of a discourse. Since every sentence in a discourse potentially can
introduce new concepts and entities which are referentially linked to previously introduced
entities, it is not possible to infer the full meaning of an individual sentence without regarding
the discourse it fits into. Kamp and Reyle states it as “the meaning of the whole is more, one
might say, than the conjunction of its parts” (Kamp and Reyle 1993, p. 59). The interpretation of
a new sentence relies both on the meaning of the sentence itself and on the structure representing
the context of the earlier sentences (Kamp and Reyle 1993, p. 59). Thus, a new sentence is
interpreted as a contribution to the existing representation of the discourse.
DRT establishes anaphoric links across sentence boundaries between anaphors in the current
sentence and antecedents in the DRS as the new sentence is processed. A very simplified outline
of what happens when the discourse in (2-2) is processed and a DRS is created, is given below
(after Kamp and Reyle 1993).
(2- 2)
a. Jones owns Ulysses.
b. It fascinates him.
(2-2a) is entered into the DRS by way of applying phrase structure rules and lexical insertion
rules in order to associate the sentence with a syntactic representation and a set of features and
values for the individual words. Some examples of assigned features are number, gender and
transitiveness (for verbs). The DRS for (2-2a) will in abridged form look like this:
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(2- 3)
Discourse referents:
x y
DRS conditions:
Jones (x)
Ulysses (y)
x owns y
Upon entering (2-2b) into the DRS, a series of actions must be performed to calculate which
entities in the DRS the two pronouns of sentence (2-2b) refer to. In the case of the sentences in
(2-2), where the DRS only contains two members, this can be determined on the basis of gender
agreement. The updated DRS will appear as in (2-4) below.
(2- 4)
Discourse referents:
x y u v
DRS conditions:
Jones (x)
Ulysses (y)
x owns y
u = y
v = x
u fascinates v
DRT offers a framework for creating and storing semantic representations of the meaning
conveyed in a natural language discourse. The theory does not, however, offer a means to
identify the referent for ambiguous anaphors, or for anaphors which require real-world
knowledge in the process of determining their referents.
2.1.1.2 Binding Theory
Binding Theory (BT) is a theoretical framework that describes syntactic conditions for intrasentential
anaphoric linking. BT offers conditions for whether a nominal expression can, must,
or must not be linked to another nominal in the sentence. Within BT, reflexive pronouns and
reciprocals are termed anaphors, while non-reflexive pronouns are called pronouns or
pronominals. This understanding of these terms will also be used in the following, when
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eferring to BT. The NP which is linked to by an anaphor or pronoun is for BT purposes the
binder of the anaphor or pronoun. In example (2-5), he is the binder of himself, while himself is
bound by he.
(2- 5)
He hurt himself.
Chomsky’s binding theory has three principles, shown in (2-6) below (after Chomsky 1981, in
Asudeh and Dalrymple 2004).
(2- 6)
A. An anaphor (reflexive or reciprocal) must be bound in its local domain.
B. A pronominal (nonreflexive pronoun) must not be bound in its local domain.
C. A nonpronoun must not be bound.
The implications of these principles, as well as that of local domain, is exemplified by example
(2-7). In (2-7a) the subclause the thief hurt himself constitutes the local domain for the anaphor
himself. As, according to principle A above, the anaphor can only be bound in its local domain,
the noun phrase the sergeant is not a possible binder. In (2-7b), the pronominal him must not
(cannot) be bound in its local domain, and can therefore be bound to the noun phrase the
sergeant. The pronominal must, however, not be bound to a noun phrase expressed in the
sentence, but can refer to a discourse referent not mentioned in the sentence.
(2- 7)
a. The sergeant said that the thief hurt himself.
b. The sergeant said that the thief hurt him.
The fact that not all possible candidates in a syntactic domain can be binders is maintained by
the requirement stating that the binder must be in a structurally dominant position to the entity to
be bound. This ensures that the noun phrase the police cannot be the binder of the pronoun
himself in example (2-8).
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(2- 8)
The sergeant’s suspect hurt himself.
Hellan (1988) suggests that the principles of standard Government and Binding theory are
primarily based on English and that they cover “a very limited subpart if what constitutes a
possible anaphoric system” (Hellan 1988, preface). He proposes several additional principles,
maybe most notably the Command Principle in which, among other statements pertaining to the
command relation, it is stated that also relations within hierarchies of thematic roles can stand in
a command relation to an anaphor.
2.1.2 Computational approaches to anaphora resolution
Automated anaphora resolution systems basically have to perform three separate tasks (Mitkov
2003):
• identify the anaphors to be resolved
• locate the candidates for antecedents
• select the antecedent from the candidate list
Different computational approaches apply different resolution factors and knowledge sources.
The process of resolving the antecedent is based on several resolution factors, which in turn
draw into account quite different sources of background knowledge. Using morphological
knowledge may be the simplest approach; gender and/or number is compared and candidates are
discounted if their gender/number does not fit that of the anaphor. Syntactic knowledge is used
to identify syntactic parallelism; the antecedent is often found in a similar syntactic position as
the anaphor. In many cases, the correct antecedent cannot be identified without the help of
semantic information. Selectional constraints is one example of semantic knowledge that can be
used to narrow down the list of candidates for the antecedent. Repeated mention of an entity in
the preceding text passage to the anaphor may indicate that this entity has a higher degree of
salience in the discourse and that it therefore is a likely antecedent for a following anaphor
(Jurafsky and Martin 2000, p. 682). Using morphological, lexical, syntactic, semantic and
salience criteria as background knowledge does not immediately suggest the most likely
candidate, but rather acts as filters to eliminate unsuitable candidates (Mitkov 2003, p. 271).
Mitkov (2003, p. 272) states that for some examples “the crucial and most reliable factor in
deciding on the antecedent” is real-world knowledge. Even the most exquisite anaphora
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esolution system will not be able to resolve anaphora of the type that needs real-world
knowledge to rule out candidates that just do not make common sense. The examples in (2-9)
below illustrate the point; without access to real-world knowledge or semantics, there is no way
to confidently resolve the antecedent of the anaphoric han (he).
(2- 9)
a. Politimannen skjøt etter morderen, og han falt.
The policeman shot at the murderer and he fell.
b. Politimannen skjøt etter morderen, og han bommet.
The policeman shot at the murderer and he missed.
2.1.2.1 Knowledge-free approaches
Botley and McEnery term anaphora resolution systems which do not consult any form of
knowledge representation in the process of identifying the antecedent of an anaphor
“knowledge-free” (Botley and McEnery 2000, p. 17). In the two following sections, it will be
shown, on the basis of two well-established syntactic algorithms for anaphora resolution, that
knowledge-free approaches that resolve anaphors without employing real-world knowledge
cannot identify different antecedents in the case of examples (1-1) and (1-2).
2.1.2.1.1 Lappin and Leass’ algorithm for pronoun resolution
Lappin and Leass (Lappin and Leass 1994, in Jurafsky and Martin 2000) offer an algorithm for
pronoun interpretation which takes into account recency and syntactically-based preferences.
The algorithm does not employ semantic preferences or background knowledge, but uses a
weighting system which reflects various syntactic features as well as salience of recency in the
discourse. When testing this algorithm on test data from the same genre as was used to develop
the weighting system, Lappin and Leass report an accuracy of 86%. Jurafsky and Martin present
a somewhat simplified part of the algorithm in the resolution of non-reflexive, third-person
pronouns (Jurafsky and Martin 2000, p. 684). The Lappin and Leass algorithm creates a
discourse model upon processing a sentence and assigns each member of the discourse model a
salience value. A set of salience factors determine the salience weight each of the members is
assigned. The aspect of recency is maintained by reducing each member’s salience value by half
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upon processing of a new sentence. (2-10) below shows the weighting system of the salience
factors in the system.
(2- 10)
SALIENCE FACTOR VALUE
Sentence recency 100
Subject emphasis 80
Existential emphasis 70
Accusative emphasis 50
Indirect object or oblique complement emphasis 40
Non-adverbial emphasis 50
Head noun emphasis 80
In the following, this algorithm will be used in an attempt to resolve the referent for the anaphor
han (he) in the second sentence of each member of the sentence pair presented in (1-1) and (1-2)
and repeated in (2-11) below:
(2- 11)
a.
Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig
kommer til å drepe igjen. Han etterlyser vitner som var i sentrum søndag
kveld.
The sergeant leading the investigation says that the perpetrator probably will
kill again. He puts out a call for witnesses who were in the city centre Sunday
evening.
b. Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig
kommer til å drepe igjen. Han er observert i sentrum.
The sergeant leading the investigation says that the perpetrator probably will
kill again. He is observed in the city centre.
When attempting to find the antecedent for the pronoun in (2-11a), all potential referents must
be collected. Since the discourse only consists of two sentences, only the first sentence is
processed, in the event of a longer discourse the referent is looked for in until four preceding
sentences. The potential referents lensmannen (the sergeant), etterforskningen (the
investigation) and gjerningsmannen (the perpetrator) are assigned salience values as shown in
(2-12) below.
14

(2- 12)
REC SUBJ EXIST OBJ IND-OBJ NON-ADV HEAD N TOT
lensmann 100 80 50 80 310
etterforskning 100 50 150
gjerningsmann 100 80 50 80 310
As the algorithm moves on to the next sentence, the values assigned in (2-12) are reduced by
half, as shown in (2-13).
(2- 13)
Referent Phrases Value
lensmann lensmannen 190
etterforskning etterforskningen 75
gjerningsmann gjerningsmannen 190
Now potential referents which do not agree in gender or number are removed. In our case, the
pronoun is han (he), which for Norwegian bokmål specifies an animate referent. According to
the preference factors in the algorithm, which check for gender and number only, the potential
referent etterforskning cannot, however, be removed. At this stage, referents which do not
satisfy intra-sentential syntactic coreference constraints will also be removed. Final salience
values are calculated by assigning values for syntactic role parallelism and cataphora. In our
case, lensmann is given extra weight due to the syntactic parallelism to the anaphor. This results
in the values shown below:
(2- 14)
Referent Phrases Value
lensmann lensmannen 225
etterforskning etterforskningen 75
gjerningsmann gjerningsmannen 190
Since lensmann has the highest salience weight, this word is also chosen as the referent for the
pronoun han. Through the processing of sentence (2-11a) it is clear that the same referent would
be assigned upon a processing of sentence (2-11b). The algorithm does not take the semantic
meaning of the sentence to be processed into account, and is therefore not able to differ between
the referent assignment in examples such as (2-11a) and (2-11b).
15

2.1.2.1.2 Hobbs’ Tree search algorithm
Hobbs’ tree search algorithm (Hobbs 1978, in Jurafsky and Martin 2000) is a pronoun resolution
algorithm based on syntactic tree structures of the sentences to be processed. Proceeding to
resolve the antecedent of a pronoun, the tree search algorithm processes syntactic
representations of all previous sentences in the discourse, as well as the sentence with the
pronoun to be resolved. The syntactic representations of the discourse, in combination with the
order the search of the syntactic structures is performed, to some degree represent a discourse
model and salience preferences. In its search for the antecedent of a pronoun, the algorithm
traverses syntactic trees in a left-to-right, breadth-first manner.
To find the antecedent for the pronoun han in the sentences in (2-11), syntactic tree structures of
the sentences are needed. The syntactic structures of (2-11) presented in Figure 1 and Figure 2
are generated by the NorGram grammar web version 3 . This is a more complex grammar than the
one used in Jurafsky and Martin’s outline of the algorithm (Jurafsky and Martin 2000, p. 689),
but as stated there, the algorithm to a large degree allows any choice of grammar. Since the
algorithm is based on searches in syntactic trees, and therefore relies on making assumptions
regarding the build-up of the syntactic structures, the grammar must be specified in any case. In
the following, the tree search as specified in Jurafsky and Martin (2000) will be carried through,
with the assumptions regarding the grammar that that implies. The syntactic trees (and their
labels) are included for illustrational purposes and should not be thought of as input for the
algorithm.
3 Generated at http://decentius.hit.uib.no:8010/logon/xle-mrs.xml 31/01-2005
16

Figure 1
17

Figure 2
In the process of identifying the antecedent for the pronoun han in the second sentence of
(2-11a), the algorithm takes as its starting point the NP immediately dominating the pronoun.
From there, it moves upwards in the tree to the first NP or sentence node. This node is called X
and the path from the pronoun to X is called p. In our case, that means that X is the topmost
sentence node (IP node). Since there are no more branches to the left of X and p, and in other
words no possible antecedents introduced earlier in the same sentence, the algorithm moves
along to the parse tree of the previous sentence. Searching left-to-right and breadth-first, the first
NP node that is encountered is suggested as the antecedent for the pronoun. In our case, this
18

means that the algorithm would propose the NP lensmannen som leder etterforskningen as the
antecedent for the pronoun han. As will be clear from examining the example sentences in
(2-11), this is a correct resolution of the antecedent in (2-11a), but not for the antecedent in
(2-11b). Parallel to the Lappin and Leass resolution algorithm, the tree search algorithm also
does not consider the semantic meaning of the sentence with the anaphor to be resolved. The
pronouns han in the second sentences of (2-11a) and (2-11b) are treated in the same way, and
lensmannen is chosen as the most likely antecedent in both cases.
2.1.2.2 Traditional approaches to anaphora resolution
As seen through the examples above, anaphors of the type that requires semantic information to
be resolved simply cannot be resolved using purely syntactic algorithms. In order to find the
antecedent for such anaphors, some sort of real-world knowledge must be consulted. Mitkov
(1999) distinguishes between traditional and alternative approaches for anaphora resolution.
The traditional approaches are those that use knowledge factors to filter out unlikely candidates
and then use preference rules on a smaller set of likely candidates, while the alternative
approaches find the most likely candidate based on statistical or AI techniques (Mitkov 1999, p.
8). The traditional approaches usually draw in the factor of real-world or domain knowledge,
often in the form of a comprehensive knowledge or domain base, in order to resolve anaphors of
the type in examples (2-9) and (2-11) above (Mitkov 2003). Such approaches are also called
knowledge-based (Botley and McEnery 2000, p. 11). In the above it has repeatedly been
emphasized that some types of anaphors cannot be correctly resolved without access to realworld
information. Carbonell and Brown’s (1988) multi-strategy approach is one traditional
knowledge-based anaphora resolution system. Their approach follows what Botley and
McEnery call “a trend […] towards the integration of several different resolution algorithms into
large-scale modular architectures” (Botley and McEnery 2000, p. 17). Their system draws on
different knowledge sources, including syntactic structure, case-frame semantics, dialog
structure and real-world knowledge. The resolution of anaphors is based on constraints and
preferences; first the constraints are applied to narrow down the list of potential antecedents and
then the preferences are applied to each of the remaining candidates (Carbonell and Brown
1988, p. 98). Real-world knowledge is realised as a set of precondition and postcondition
constraints. These constraints for example determine that the object given no longer is in the
possession of the actor after a successful act of giving has been carried out. The main problem
19

with such an approach is stated by the developers: “the strategy is simple, but requires a fairly
large amount of knowledge to be useful for a broad range of cases” (Carbonell and Brown 1988,
p. 97).
Generally speaking, the knowledge bases that knowledge-based systems for anaphora resolution
rely on are difficult to represent and process, and require a considerable amount of human input
(Mitkov 2001, p. 110). The information is structured using different frameworks; often each
anaphora resolution system structures its knowledge base in a system-specific manner. Rather
than giving an outline of various specific methods belonging to the traditional approaches, some
of the formats used for knowledge representation are briefly mentioned below. Several
frameworks have been developed to cope with the need for a formalism to represent real-world
or domain knowledge. Most of these have been part of specific anaphora resolutions systems
and have not constituted independent frameworks for the representations of real-world
knowledge.
Minsky’s Frames (Minsky 1975, in Botley and McEnery 2000) is a framework for representing
knowledge about stereotyped objects and events. The frames are dynamic in the sense that the
information they hold about a particular object or event can change if new information is
encountered. Input into the system is interpreted in accordance with the information present in
the frames; the frames generate expectations about the input (Botley and McEnery 2000, p. 12).
In the case of a “shooting frame” being evoked upon processing of the sentence in (2-9a), the
expectation that if somebody misses, it is likely to be the same person that also was doing the
shooting, is created. Following such an expectation, it is easy to identify the correct antecedent
for the anaphor. Schank’s Scripts (Schank 1972, in Botley and McEnery 2000) have some
similarity to Minsky’s Frames, but are primarily used to represent knowledge about events
which do not undergo change (Botley and McEnery 2000, p. 12). Information about role
assignment and the sequence of events in given contexts is represented in the script.
2.1.2.3 Alternative approaches to anaphora resolution
Hand-coded knowledge bases that aim at representing real-world or domain knowledge are
expensive and labor-intensive to build and maintain. As a consequence, the focus has shifted
toward systems that rely less heavily on world knowledge in the last 15 years (see Mitkov 2003
20

for an overview). Many of these systems incorporate semantic and real-world knowledge, but
use methods that enable the collection of this information to have a high degree of automation
(Baldwin 1997; Dagan and Itai 1990; Dagan et al. 1995; Nasukawa 1994; inter al.). Mitkov
(2003) terms these systems knowledge-poor and attributes their growth in number in recent
years to the fact that corpora and similar electronic linguistic resources have become better,
larger and more available. Some of these systems do not really attempt at building a world- or
domain knowledge base (Baldwin 1997; Nasukawa 1994), but rather look at features such as co-
occurrence patterns in the text itself, while others integrate corpora and use them as a form of
abstract knowledge base (Dagan and Itai 1990; Dagan et al. 1995).
Among the different “alternative” approaches, Dagan and Itai’s (1990) statistical approach,
Dagan et al.’s (1995) estimation of unseen patterns and Nasukawa’s (1994) knowledge-free
method are of particular interest for this project. Dagan and Itai’s (1990) method is that of using
co-occurrence patterns observed in a corpus as a type of selectional restrictions. Co-occurrence
patterns observed in a large corpus are thought to reflect the semantic constraints that apply to
natural language. Candidates for antecedents for the anaphor it are identified in the text and put
in the place of the anaphor to be resolved. This produces co-occurrence patterns that are checked
against the corpus. Subsequently the candidate present in the most frequently occurring cooccurrence
pattern is chosen as the antecedent. This method relies on a large corpus, as only
patterns which actually have been seen in the corpus are considered. Infrequent patterns will not
be picked since they generally speaking will not feature on the top of the pattern list. Dagan et
al. (1995) offer a solution to this problem by presenting a similar method which also estimates
the probability of co-occurrence patterns that have not been observed in the training data. They
state the importance of distinguishing between probable and improbable unobserved cooccurrence
patterns and emphasise that the “distinctions ought to be made using the data that do
occur in the corpus” (Dagan et al. 1995, p. 164). Anologies are made between specific unseen
co-occurrence patterns and observed co-occurrences which contain similar words, determining
word similarity by a similarity metric. Patterns that contain similar words to the target word and
that have been observed in the training data are used to calculate how likely the target word is to
occur in the same pattern. Nasukawa (1994) presents a resolution rate of 93,8% in an even
knowledge-poorer method for pronoun resolution. Instead of drawing information from a
corpus, word frequency and co-occurrence patterns in the text itself are used to filter out the
most likely candidate for the antecedent. In Nasukawa’s approach, inter-sentential data is
21

exploited in the process of resolving the pronoun it. The likelihood of the antecedent is
determined statistically and the antecedent candidate with the highest value is selected by the
system. The approach uses a syntactic-based heuristic rule for the selection of the antecedent.
Nasukawa states that approaches using real-world knowledge are not large-scale enough yet to
be of use in broad-coverage systems and attempts at extracting information corresponding to
case frames in world knowledge from the texts to be processed (Nasukawa 1994, p. 1157). In
this way, collocation patterns are used as a form of world knowledge for the domain of the texts.
As has been outlined in the introduction, this thesis describes a method that can aid in the
resolution of the anaphoric expressions that require real-world knowledge to correctly resolve
their antecedents. The method automatically extracts and classifies nominal arguments, resulting
in associated classes of similar words. This is a knowledge-poor method in the sense that it does
not require a comprehensive knowledge base to be built, but rather uses data and co-occurrence
patterns from a corpus to find the most likely antecedent from a list of possible candidates found
in a text.
2.1.3 Anaphora resolution and text summarisation
As already mentioned, several NLP applications need a reliable means to resolve anaphoric
expressions and identify coreferences. In the field of text summarisation, which belongs to the
domain of the KunDoc project, anaphora resolution is vital for the process of finding
coreferential chains, identifying discourse structure and ultimately producing a coherent
summary. Systems for automatic text summarisation need to make a number of choices
regarding the resolution of anaphoric expressions. Mani (2001, p. 70) identifies “dangling
anaphors” as a coherence problem in automatic summaries; without a means to resolve
anaphoric expressions, the summary may contain anaphors, but not the antecedents they refer to.
This disturbs the coherence in the summary; not all the information that the reader needs is
present in the summarised text. The (constructed) example below illustrates this: consider the
full text example in (2-15a) in connection with the summarised version in (2-15b). Both
instances of the pronouns han (he) in the summarised version in (2-15b) do not have an
identified reference in the text. For a reader presented only with the summary, it is highly
unclear what these pronouns refer to.
22

(2- 15)
a. Politiet etterlyste i dag tidlig en syklist i
forbindelse med drapet på 23 år gamle Anne Slåtten. I
formiddag meldte han seg til politiet, skriver bt.no
- Jeg har foreløpig ikke klarhet i hva han har sagt,
forteller lensmannen i Førde Kjell Fonn.
This morning the police instituted a search for a biker
in connection with the murder of 23-year-old Anne
Slåtten. This morning he reported to the police, writes
bt.no
- For the time being I am not in the clear about what he
has said, tells the sergeant in Førde Kjell Fonn.
b. I formiddag meldte han seg til politiet, skriver bt.no
- Jeg har foreløpig ikke klarhet i hva han har sagt,
forteller lensmannen i Førde Kjell Fonn.
This morning he reported to the police, writes bt.no
- For the time being I am not in the clear about what he
has said, tells the sergeant in Førde Kjell Fonn.
Another reason why anaphora resolution is important for text summarisation is that the methods
used for retrieving relevant sentences for a summary perform more accurately if anaphoric
references to central concepts also are considered (Mitkov 2003, p. 276).
The emergence of knowledge-poor and corpus-based approaches for anaphora resolution
suggests that the representation of real-world knowledge not necessarily has to have the form of
a human-made system. Alternative approaches show that information available from the text to
be analyzed or from larger bodies of natural language text, can be used to give information that
resembles that of real-world knowledge. The following section explains this notion of using
contextual information to find intuitions about world knowledge.
23

2.2 Finding meaning in the context
“You shall know a word by the company it keeps!” Firth (1957, p. 179)
“The meaning of entities, and the meaning of grammatical relations among them, is related to
the restriction of combinations of these entities relative to other entities.” Harris (1968)
2.2.1 The distributional approach
The semantic meaning of a word is often readily suggested from the lexical context in which it
occurs. This is an idea fronted by many scholars, starting with Firth (1957) and Harris (1968).
Human beings use the context of a word in the process of deciding the semantic meaning of the
word. When encountering an ambiguous word, the language user has a finite number of possible
meanings to consider. By examining the environment that the ambiguous word exists in, the
language user finds clues toward deciding which of the possible meanings that are applicable.
The same mechanism applies when a language user is confronted with a novel word; by
observing the usage of the word, preferably over several instances, a human being is able to
induce the semantic meaning from the setting the word occurs in. This is in accordance with the
Distributional Hypothesis as proposed by Harris (1968) and contributes to explaining the fact
that humans rarely have problems identifying for example what an ambiguous word means, or
what entity in the discourse a pronoun refers to. The fact that properties in a word’s linguistic
environment can contain information about the meaning of the word is a useful tool for the
semantic comparison of words. A word which is used within a limited thematic domain is likely
to be used in a sense specific to the contextual setting, or domain, in which it occurs. This entails
that the linguistic environment in which the word exists also gives information about the
meaning of the word. Words that appear in the same linguistic setting in texts that describe the
same theme may have similar or related meanings as well. Texts belonging to the same domain
will to some extent contain information about the same things, and as such also contain
semantically similar words which are used in similar ways. Following this line of thought, it
should be possible to gain relevant information about which words to expect in specific
positions in a text by way of looking at the context patterns they should fit into. Thus, words that
occur in limited-domain texts can be classified relative to how they combine with each other.
The idea that the contextual environment can give clues about the semantic meaning of a word is
clearly not a new one, considering the quotes of Firth and Harris in the introduction to this
24

section. The theory dates back to the empiricists of the mid-twentieth century. Linguistic theory
in the first half of the twentieth century was to a large degree predominated by empiricism.
Linguistic thought, particularly in the United States, but also in Europe, was strongly influenced
by the positivism of the behaviourist philosophy. Bloomfield is regarded as one of the chief
advocates of linguistic positivism, and his interpretation of linguistics predominated American
linguistics in the 1930s and 1940s (Robbins 1997, p. 237). The positivistic/behaviouristic view
on linguistic science put emphasis on the observable. Reliable facts could only be found through
objective observation of data, and only phenomena which could be empirically experienced by
any observer were considered valid data for further analysis. Robbins states that the favoured
model of description of the time was that of distribution; for some linguists the notion of
linguistic description coincided with the statement of distributional relations (Robbins 1997, p.
239). He also attributes the fact that there was little emphasis on the study of semantics in the
early twentieth century to Bloomfield’s dismissal of the possibilities of an empirically based
study of this field. Since the analysis of meaning requires non-linguistic knowledge as well,
semantic analysis was termed less ideal for empiricist methods. While the study of semantics
previously had aimed at creating an exhaustive description of what is referred to by a linguistic
entity, Firth represents a challenge to this way of thinking. His “contextual theory of language”
introduced a move in semantics, toward a statement of meaning as a function of how words are
used (Robbins 1997, p. 247). Together with Harris, Firth represents the distributional approach
to finding semantic meaning. Within this approach, meaning is treated as semantic functions
related to contexts of situation. This way of analysing meaning is data-driven in the same sense
as empiricist approaches in other fields of linguistics and is strongly connected to the positivistic
philosophy of science predominant in this time. However, using bottom-up methods as a means
to formulate theories of linguistics is a direction that was more or less abandoned after
Chomsky’s criticism of the structuralist approaches. Chomsky challenged the philosophical and
scientific foundation of the Bloomfieldian canon through his proposal of the transformationalgenerative
grammar. He dismissed the behaviouristic approach to language as the unacceptable
product of the strong empiricism of the Bloomfieldian behaviourist school. The shift from
empiricism to rationalism marks an important turning point in linguistic theory (Robbins 1997,
p. 260). Botley and McEnery state that Chomsky, and the generation of linguists following his
theories, represent a knowledge-driven approach with the goal of formulating linguistic theories
(Botley and McEnery 2000, p. 24).
25

The method of describing semantic meaning by looking at the distribution of text in context has
more or less been abandoned in the decades following the shift of paradigm from empiricism to
rationalism. Semantic analysis has been approached through new methods within linguistic
theory, terming the meaning-is-use approach as too simple. In recent years, however,
computational linguistics has brought some of these old ideas forward again. This is mainly due
to the increasing availability of large, computer-readable corpora and powerful processing tools
that reliably can perform operations on large data sets. The emergence of corpus approaches is a
move away from the Chomskyan view toward an emphasis on actual observable linguistic
behaviour (Botley and McEnery 2000, p. 24). Thus, the bottom-up approaches of Firth and
Harris are again in fashion; by using corpora, computational linguists today are able to look at
actual occurrences of data and use these to develop theories of linguistic performance. Leech
argues that corpus linguistics is not a linguistic theory, but rather a methodology (Leech 1992, in
Botley and McEnery 2000, p. 23). Rather than being primarily theoretically founded, corpus
linguistics as a discipline focuses on linguistic performance and description, as found in actual
occurrences of natural language text. One can say that Firth and Harris’ ideas have received a
pragmatic renaissance; probably for a large part because of the available computational tools.
Whether these new appliances of the distributional theories from the 1950s reflect a
reconsideration of the usefulness and theoretical foundation of them, or whether they merely
show that computational linguistics is a more pragmatic than linguistic theoretically founded
science, is a discussion that is far out of the scope of the present work. What can be stated,
though, is that the notion of using a word’s context to find out something about the meaning of
that word, is an approach that seems to provide interesting results regarding semantic meaning,
regardless of the motivation of such an approach. The type of semantic information available
from the context is, however, not necessarily of the same type as that referred to when speaking
of the semantic meaning of a word. Information obtainable from looking at distribution over
several contexts rather provides a measure of semantic relatedness or closeness. Instead of
providing a means to obtain or define the direct semantic meaning, methods that rely on
contextual distribution can return words with the same or different meaning as a target word.
26

2.2.2 Different types of context
So far, we have argued that using context as a tool to indicate the semantic meaning of a word is
a useful method in linguistics. The method’s theoretical foundation dates back to the middle of
the twentieth century, but has not been pursued much in the last few decades. Even though the
linguistic foundation of this method has been discussed, the advance in computational resources
in recent years has brought this approach forward again. However, this being said, the different
types of context that can be taken into consideration have not been discussed so far in this thesis.
Agreeing on the fact that the semantic meaning of a word is suggested from the linguistic
context in which it occurs, or “the company it keeps”, supports the notion that different words
used in the same context are semantically similar. It does, however, not provide a means for
calculating the degree of this similarity or even finding out exactly which words are similar to
each other. Depending on the information that is desired to obtain about a target word, different
context types mirror different aspects of the semantic meaning of a word. Any approach that
attempts at describing semantic meaning based on the contextual distribution of words in a text
collection must first define the type of context that best will reflect the desirable information.
Somewhat simplified, we distinguish between topical context and local context.
2.2.2.1 Topical context
Topical context (Miller and Leacock 2000), or document context, is a quite wide term that
covers what we could call the “wide conception” of what context is. All other content words
which occur in the same environment as a target word are considered to make up the context of
the word, and following the discussion above, contribute to indicating the semantic meaning of
the target word. A target word’s contextual environment can be further specified depending on
the purpose; in short, the context simply is all the words which occur within a context window
of varying size. The window can be set to cover a certain number of words before and after a
target word, or also to consist of the entire document the target word occurs in. Different
parameters determine the weighting of each word found within the context window; for example
words can be weighted according to their distance from the target word. One extreme way of
looking at topical context might be the bag of words model, where a document is seen as an
unordered collection of words, and the words are weighted by the number of times they occur in
27

a document. In a more narrow sense, topical context can be limited to only consisting of the
other words in the same sentence as a target word.
The extraction of topical context does not draw on syntactic or semantic information and
therefore does not provide an indication of the relationship that the words in the context have to
each other or to the target word. It is therefore not possible to say anything specific about
semantic similarity based solely on the occurrence of words in the topical context. As the name
indicates, this type of context gives information about the topic, or the domain of the text that
the target word exists in.
Consider the words in example (2-16) as the topical context for the target word sykepleiestudent
(student nurse). The context words are words which occur more than once in a short newspaper
text from the text collection used in this project. Even with such a rudimentary method of
selecting the words in the topical context, it is clear that this type of context provides cues to the
thematic domain that the target word occurs in. The topical context does not, however, provide a
means of finding words that are semantically similar to the target word. No close synonyms are
retrieved, but rather words belonging to the same discourse domain as the target word.
(2- 16)
kvinne
funn (subst)
funnet (partisipp)
død
Førde
politi
leteaksjon
2.2.2.2 Local context
woman
finding (noun)
find (participle)
dead
Førde
police
search party
Local context provides a more finely tuned way of looking at semantic similarities as expressed
through the distribution of words in a text. In its simplest form, a word’s local context consists
of its immediate surrounding words; that is the words immediately preceding and following a
target word. The notion of local context can also be extended to include information about
syntactic and grammatical properties that belong to the target word and its immediate
28

neighbours. For example, a target word’s local context can be seen as its subject and object, or
as the adjective preceding it.
Several studies show that classifying words based on the local context in which they occur gives
information about the semantic meaning of the words, rather than their membership within a
thematic domain, as found when examining the topical context (Hindle 1990; Grefenstette 1992;
Lin 1998; Lin and Pantel 2001; Pereira et al. 1993; inter al.). This indicates that access to
features within a word’s local context can contribute to saying something about the meaning of
the word and ultimately to act as a foundation for the formation of concept groups of
semantically similar words. Distributional representations based on a word’s local context are
useful for measuring the semantic similarity of words. Lin (1997) exploits this in an algorithm
for word sense disambiguation and states that local context gives crucial clues about the
meaning of a word following the intuition that:
“Two different words are likely to have similar meanings if they occur in identical local
contexts.” (Lin 1997, p 64).
Since the local context can comprise syntactic and semantic information, it provides a means to
access different information relevant to the type of analysis that will be performed on the
material. Several approaches describe methods for finding similar nouns based on the
distributional patterns of words in the local context (Hindle 1990; Grefenstette 1992; Lin 1998;
Lin and Pantel 2001; Pantel and Lin 2002; Pereira et al. 1993; inter al.). These methods classify
words in accordance with their distributional patterns, not using hand-coded semantic
knowledge as a basis, but rather inferring the required knowledge from a text corpus as part of
the analysis process. The approaches all adopt different methods for judging the similarity of
words. Below, some of the approaches to finding similar words are described briefly; the
similarity metrics, however, will not be discussed in this outline.
Hindle (1990) shows that the contextual distribution of words provides a useful semantic
classification, also in the event of an automated classification process with no human
intervention. His method examines predicate-argument structures in a large corpus and
automatically classifies nouns into semantically similar sets on the basis of the predicates they
combine with. The similarity between nouns is measured as being a function of mutual
29

information estimated from the text. Hindle’s results show that semantic relatedness can be
derived from the distribution of syntactic forms (Hindle 1990, p. 274). This is a similar approach
to the one taken in the present work, if on a substantially smaller scale. Hindle (1990) addresses
the data sparseness problem by estimating the probability of an unseen event by comparing it to
similar events which have been seen. Grefenstette (1992) presents a method which looks for
context patterns in large domain-specific corpora and finds similar words relative to how a target
word is used in a specific text or domain. His program SEXTANT uses syntactically derived
contexts and estimates the similarity of two words by considering the overlapping of all the
contexts associated with them over a large corpus (Grefenstette 1992, p. 325). As a result, a
word’s context consists of all the words co-occurring with it in the corpus. Pereira et al. (1993)
also report a method for clustering words according to their distributions in given syntactic
contexts. In their approach, nouns are classified based on their syntactic relations to predicates in
the corpus. The method enables the automatic derivation of classes of semantically similar
words from a text corpus and produces clusters the authors term “intuitively informative”
(Pereira et al. 1993, p. 190). Lin and Pantel (2001) present the unsupervised algorithm UNICON
for the creation of groups of semantically similar words. Their approach examines collocation
patterns consisting of dependency relationships, and employs a method for selecting significant
collocation patterns. Those dependency relations which occur more frequently than if the words
were independent of each other are selected as collocation patterns. This approach is further
developed in Pantel and Lin (2002). Here, clusters which are relatively semantically different
are initially identified and a subset of the cluster members are used to create so-called centroids,
which represent the average features of the subsets. Subsequently new words are assigned to
their most similar clusters. A word can be assigned to several clusters, each cluster
corresponding to a sense of the word.
2.2.3 Context and selectional restrictions
In the above it has been argued that a given word will tend to co-occur with a limited class of
other words, and that this information can be exploited to find words that are similar in meaning.
One of the reasons for this expected occurrence of similar words in similar contexts, is that
predicates to a certain extent limit the semantic properties of the arguments that they can
combine with. This behaviour is captured through the notion of selectional restrictions, which
define how a predicate restricts the class of arguments that can combine in a specific position
30

with it. Selectional constraints allow a predicate to specify semantic restrictions on its arguments
(Jurafsky and Martin 2000, p. 512). This accounts for the intuition that only a certain class of
words can occur in a specific argument position to a given predicate. In the case of a verb such
as avhøre (interrogate, take statement from) a possible selectional restriction for the first
argument could be that it must represent a human. Jurafsky and Martin formulate it like this:
interrogate restricts the constituents appearing as the first argument to those whose underlying
concepts can actually partake in an interrogation (Jurafsky and Martin 2000, p. 512, slightly
modified).
More nuanced intuitions of selectional restrictions can be obtained by combining the knowledge
of distribution in context and that of semantic restrictions placed on arguments by the predicate.
This thesis applies a practical approach in order to find properties of the selectional restrictions
of predicates within a limited thematic domain. Without aiming at formulating a comprehensive
list of the selectional restrictions that apply within the domain in question, it is possible to obtain
a list of examples that illustrate certain properties of the selectional restrictions. This is an
extensional approach; by examining the structure of a set of arguments that all occur in the same
contextual environment, for example as the first argument of a certain predicate, it is possible to
draw certain conclusions about the selectional restrictions placed by the predicate. The aim of
this project is not to define the selectional restrictions of the predicates in the data set, but rather
collect a list of examples of valid restrictions for the domain and examine these. It is obvious
that selectional restrictions also vary over different thematic domains; the allowed first
arguments of a predicate will be very different in a formal text than in a fairy tale for children.
This is again the intuition outlined in the first section of this chapter; words are used in different
ways depending on the thematic domain they exist in. The distribution that classes of
semantically similar arguments show within a limited domain may therefore very well be seen
as a type of selectional constraint. To exemplify this, consider the domain used in the present
work; newspaper texts concerning a criminal case. Considering the constructed phrases in
(2-17), the first two are valid for the domain in the sense that they exemplify structures which
are found in the data set, while the third is in violation of the selectional constraints assigned by
the verb within this particular thematic domain. In the event of a killing within the domain in
question, it can be expected that a perpetrator or a man has the thematic role of actor, but it has
not been seen in the data material that a student can initiate this action.
31

(2- 17)
gjerningsmannen drepte kvinnen
mannen drepte kvinnen
studenten drepte kvinnen
the perpetrator killed the woman
the man killed the woman
the student killed the woman
The example above illustrates how context within the present work is used to formulate a notion
of selectional restrictions. These can later be used to say something about which argument can
be expected to feature in a specific contextual environment, and thus functions as a type of realworld
knowledge for the domain of the text collection. With specific reference to anaphora
resolution, these selectional restrictions can be used to give an indication of the most likely
antecedent for an anaphor which normally would require access to real-world knowledge in
order to be resolved.
32

3 From text to EPAS – the extraction method
This chapter describes the extraction method used in this project. The method extracts EPAS
(elementary predicate-argument structures) from a text corpus consisting of newspaper texts
collected from the internet.
3.1 Selecting the texts
Specifying the requirements for a suitable text collection is not as trivial as it may seem. To
make sure that the extracted EPAS would produce semantically valid results when classified, the
texts from which the structures were extracted had to fulfil certain requirements. Since the
classification builds on the distributional hypothesis and relies on EPAS which show distribution
particular to a restricted domain, initially, the most important specification for the texts was that
they all had to belong to the same thematic domain. As such, the main focus in the requirements
specification for the text collection was that of one closed thematic domain. But how exactly
does one define the notion of a thematic domain? The first test set collected for the project
consisted of factual prose texts dealing with roughly the same field. These texts, however,
proved to be quite unsuitable for the later analysis, for reasons that will be explained in the
following.
It is clear that certain specifications must be fulfilled in the text collection from which the EPAS
are derived. Texts displaying longer discourse chains are most suitable for the purpose of this
project. One thematic domain must be described over several paragraphs, or preferably over the
entire course of the discourse in the text. In order to extract the desired information from the
texts and subsequently test if useful information has been extracted, the presence of anaphora, or
referring expressions, in the text is needed. This entails the need for pronouns in particular. As
such, texts containing discourse with a certain amount of concrete content were particularly
useful for my purpose.
Texts that are too vague, both with regards to their textual content and to their membership in a
particular thematic category, were not suitable for the purpose of this project because they do
not contain the type of theme-specific selectional constraints we are interested in extracting. One
reoccurring problem in the text collections tested for the project was a too small degree of
information expressed in full text, and very much information present in bullet lists, tables and
33

other similar constructions. This information was only accessible after manual editing of the
texts, and was even then often not useful since precisely the desirable discourse chains are
avoided by use of this type of textual shorthand constructions. The information present in bullet
lists and tables in the unedited text is most often not formulated in well-formed sentences and
usually the use of referring expressions and pronouns is avoided. Such texts are also not
immediately suitable for parsing, thus making it complicated to extract EPAS (semi-)
automatically.
As mentioned above, selecting the texts to be analyzed and creating the text collection to be the
basis of the classifications in the project was a task that is not to be underestimated. Several
different types of texts were experimented with in the attempt of finding a text type that satisfied
the following criteria, in addition to being available for collection on the internet:
• Limited and naturally confined thematic domain
• Relatively long chains of discourse
• Fairly high occurrence of anaphora, pronouns in particular
• Several paragraphs where the same phenomenon is discussed
• Low occurrence of tables and illustrations, ideally all the information in the texts should
be expressed in complete and grammatical sentences
The text type that fulfilled these criteria to the highest degree were news texts. By picking
newspaper articles that all concerned the same theme, the criteria of a limited domain was
satisfied. The articles, as provided on the internet, additionally fulfilled all the other
requirements which had been set for the text collection. For this project, articles concerning a
criminal case in the small town Førde on the west coast of Norway were chosen, mainly because
this was a very big case in the Norwegian newspapers and a large number of articles have been
written on the subject. The articles were selected from the newspaper Verdens Gang (VG) in
June and July 2004.
34

3.2 Predicate-argument structures
"Not the same thing a bit," said the Hatter. "Why, you might as well say that 'I see what I eat' is
the same thing as 'I eat what I see'." from Alice in Wonderland by Lewis Carroll.
For the purposes of the subsequent classification phase, a meaning representation that would not
allow for ambiguity or vagueness was desirable. Using the term EPAS, rather than referring to
the verb and its subject and object, contributes to normalising and generalising the data. The
motivation for choosing elementary predicate-arguments structures, or EPAS, as the
representation of the meaning structures in the text collection will be explained in the following.
By choosing EPAS as meaning representation, the focus of the structure is the verbal predicate.
Instead of structuring the semantic representations extracted from the texts according to the
grammatical roles and the formal function each word holds in the sentence, we look at how the
verbal predicate combines with arguments. This is closely related to the idea of thematic roles,
where the focus is on which roles the entities in a sentence occupy. It is suggested that “verbs
must have their thematic role requirements listed in the lexicon” (Saeed 1997, p. 140) and as
such that each verb has a predetermined set of possible argument frames. Thematic roles span
over a wide range that describes the various roles the entities in a sentence can occupy. Using
Saeed’s hierarchy of thematic roles, the agent is the initiator of action, while the patient and the
theme are the entities an action is performed on. For Norwegian and English, there is a tendency
for subjects to be agents and direct objects to be patients and themes (Saeed 1997, p. 145). This
tendency can be altered by the speaker as a result of stylistic choice or desire to alter the
information structure, for example by using passive verbal voice. The assignment of thematic
roles to particular positions in a sentence is closely connected to the hierarchical structure of the
thematic roles. There is a hierarchy of defined thematic roles for each sentence position; the
hierarchy in (3-1) exemplifies the preferred order of roles in subject position (Saeed 1997, p.
146).
(3- 1)
agent > recipient/benefactive > theme/patient > instrument > location
The structuring of a semantic representation into predicates with belonging arguments does,
however, not express exactly the same information as the assignment of thematic roles does.
35

When using predicate-argument structures, the definitions of argument 1 and argument 2
presupposes the existence of an underlying semantic hierarchy which defines the roles for agent
and patient. For example, argument 1 can be defined as to always representing the agent in the
sentence. An important distinction between the predicate/argument paradigm and that of
thematic roles, is that by using agent/patient as the core of the semantic representation, the
semantic classification does not focus on the predicate and its associated arguments. On the
other hand, in a predicate/argument classification, the definition of the individual arguments
does not directly consider the thematic roles. Since a semantic hierarchy has more well-defined
roles than expressible with arguments, different instances of argument 1 will not have exactly
the same semantic role, depending on the predicate they co-occur with.
For the purpose of processing the extracted structures, it is useful that the structures are in a
simplified form and also that structures that semantically convey the same information, but are
expressed in a different manner syntactically, are represented with the same structure. This is in
alignment with the doctrine of canonical form, which states the usefulness of letting linguistic
constructions which display the same meaning content give rise to the same meaning
representation (Jurafsky and Martin 2000, p. 507). By using a normalised form of
representation, such as EPAS, for the structures extracted from the text, as well as founding the
extraction method on semantic representations of the analysed texts, the generation of a
generalisable data set is achived. Active and passive constructions with equivalent semantic
meaning will be treated in the same way and will receive identical meaning representations. This
can be seen in the following example:
(3- 2)
a. Morderen drepte kvinnen.
The murderer killed the woman.
b. Kvinnen ble drept av morderen.
The woman was killed by the murderer.
c. Kvinnen ble drept.
The woman was killed.
Sentence (3-2a) and (3-2b) in essence convey the same information and only differ with
reference to verbal voice. The use of diathesis alternations of active and passive voice gives the
36

speaker flexibility with regards to the relationship between grammatical structure and thematic
roles. The use of passive versus active voice does not really alter the semantic content of a
sentence, but represents a difference in the information structure of a sentence. Differences of
this kind are not relevant for the present purposes, and will not be reflected in the extracted
structures. The word kvinnen (the woman) has different syntactic roles in the acitve sentence in
(3-2a) and the passive sentence in (3-2b), but has the same thematic role. Regardless of the fact
that the phrase kvinnen (the woman) in (3-2b) represents a subject, while it represents an object
in sentence (3-2a), both expressions have the thematic role of patient, or the entity acted upon.
For the purposes of the present work both sentences will be represented by the single EPAS
shown in (3-3):
(3- 3)
Predicate Argument 1 Argument 2
drepe morder kvinne
kill
murderer
woman
Sentence (3-2c) is in passive voice and the subject from the active-voice sentence (3-2a) is not
present. The formal subject of the sentence, “woman”, is logically a patient, and refers to the
entity on which the activity of killing is performed. As such, this sentence will be represented by
an EPAS which lacks its argument 1:
(3- 4)
Predicate Argument 1 Argument 2
kill woman
Extraction methods that are not based on a syntactic parse of the original texts do not have
access to semantic relations within a sentence. This means that such methods must rely on more
superficial structures, such as part of speech tags, and will not have the same degree of accuracy,
or finesse, in the actual extraction of the meaning structures. Since the present work aims at
providing results that can be useful as part of an anaphora resolution system, it is particularly
important that the results obtained can be generalised as much as possible. Especially since the
individual elements of the extracted structures will be used for subsequent processes, it is of
high importance that they do not contain errors or irregularities as a result of the extraction
37

process. To be as useful as possible, the meaning structures should be normalised and
generalisable.
The examples above show how normalisation through use of EPAS realises the concept of
canonical form to some degree and seems particularly useful for the purpose of the present
work. By using grammatic relations such as subject and object as reference points, semantically
equivalent sentences, such as (3-2a) and (3-2b), would be given different meaning structures due
to the difference in verbal voice. Structuring the meanings conveyed with the sentences in (3-2)
within a grammatical relations paradigm would make it necessary to mark the verbal voice as
well as the grammatical relations. In addition, active and passive structures would have to be
treated differently in the subsequent analysis. Basing the extraction merely on syntactic
properties of the sentences in the corpus would make the extracted material very difficult to
classify, mainly because similar meanings would be represented differently.
The advantages of a normalised and generalisable dataset is further clarified by the following
example. Upon a simple grammatical analysis, the sentences shown in (3- 2) can be categorised
based on the syntactic roles predicate, subject and object. The result of such a classification is
shown in examples (3-5) and (3-6):
(3- 5)
predicate subject object
a. drepe morder kvinne
kill
b. drepe
kill
c. drepe
kill
murderer
kvinne
woman
kvinne
woman
woman
murderer
murderer
?
The structures in (3-5) above can be extracted upon part of speech tagging of the sentences in
(3-2). The active and passive predicate receives the same structure, and as no semantic
information is available, the structuring of the arguments is in accordance with their status as
subject or object. Attempting to classify these subjects and objects based on their co-occurrence
with the predicate produces groupings of words which are not directly generalisable. Murderer
38

and woman occur together both in subject and object position, not reflecting the preferred
selectional constrictions within the domain.
(3- 6)
predicate subject object
a. drepe morder kvinne
kill murderer woman
a. drepes kvinne morder
is-killed woman murderer
b. drepes kvinne ?
is-killed woman
Example (3-6) provides a more elegant structuring. Because an extraction method based on
syntactic relations is unable to generalise over verbal voice, two separate predicates are
extracted, one for the passive and one for the active voice. Even though logically, the same
action is performed on the entity the woman in both sentences in (3-6), a method as outlined
above would not allow for a straightforward interpretation of this. The generalisation between
active and passive versions of the same sentence is lost in such an approach. This would result in
a higher number of predicates, and therefore in a less generalisable data material. It is likely that
results as outlined above would also be of less use as a referent guessing helper in an anaphora
resolution system, precisely because of the lower level of generalisability.
3.2.1 What is represented in the EPAS?
Jurafsky and Martin (2000, p. 510) state that all languages have predicate-argument structures at
the core of their semantic structure. They further describe that the grammar organises the
predicate-argument structure and selectional constraints restrict how other words and phrases
can combine with a given word. In this project, a simplified version of predicate-argument
structures is used as meaning representation. The EPAS, or meaning representations, are limited
to consist of two nominal arguments at the most. Either one of the arguments in an EPAS may
be empty/unidentified. This means that the EPAS extracted from my texts will belong to one of
the following three patterns:
39

(3- 7)
a. predicate, argument 1, argument 2
b. predicate, argument 1, ?
c. predicate, ?, argument 2
The reason for letting the EPAS consist of a maximum of two arguments is not primarily a
fundamental decision, but rather an emergence from the empirical material that the data
structures were collected from. When extracting EPAS from the data collection, the resulting
structures consisted of a predicate with maximally two arguments. It is probable that there
generally are fewer occurrences of predicates with more than two belonging arguments, and
since my data collection is quite small, such occurrences do not feature in it.
Only nominal arguments are featured in the EPAS; entailing that sentences with a nominal
clause as object will be extracted as an EPAS lacking argument 2. This is clarified through the
examples below:
(3- 8)
Et vitne opplyste at hun hadde hørt høye rop.
A witness informed that she had heard loud screams.
The sentence in example (3-8) above will yield the following three EPAS:
(3- 9)
a. høre, vitne, rop
hear, witness, scream
b. høy, rop, ?
loud, scream, ?
c. opplyse, vitne, ?
inform, witness, ?
(3-9c) does not display an argument 2 despite the fact that the original sentence has a nominal
clause as object. The main reason for this choice of representation is that the subsequent
classifying phase aims at creating classes of nominal arguments, based on the verbs they co-
40

occur with. Arguments which are unlikely to represent relevant and interesting information for
our classification are therefore omitted. In cases where a verbal predicate takes a nominal clause
or a sentence as its argument, it is unlikely that the predicate selects the argument based on (for
us) semantically interesting selectional restrictions. A verb can show restrictions in the selection
of an argument, presenting us with the possibility of saying something about this argument
relevant to the environment it occurs in. The same restrictions cannot be expected to apply in the
cases where the verb takes a sentence as its argument. As a consequence of this, the meaning
representations are limited to dealing with arguments which can be represented by single
symbols and which do not refer to clauses or sentences.
In order to extract all EPAS present in the texts, it will also be necessary to extract non-verbal
predicates. These will generally speaking correspond to adjective-noun combinations in the text.
This way, phrases of the type “the statement is important” or “the important statement” will both
produce the following EPAS:
(3- 10)
Predicate Argument 1 Argument 2
important statement ?
The process of extracting the EPAS is a challenging part of the project, especially since the
available tools for Norwegian are not robust enough to make this a trivial and straightforward
task. Regardless of which method is used to extract the EPAS, it is evident that large part of the
work on this project must be dedicated to the development of a suitable method for the
extraction of them. For several reasons, it is desirable to develop an extraction method that is as
automatic as possible. Most importantly, such a method saves a lot of time, but another
important aspect is that more manual extraction methods easily could become subjective and
less systematically concise. The next two sections will discuss the task of extracting the EPAS in
more detail.
41

3.3 Parsing with NorGram
To be able to extract the EPAS from the text in a semi-automatic fashion, some sort of linguistic
analysis of the texts is needed. One problem with working on a small language like Norwegian
is that the linguistic tools you might need in the process just are not fully developed yet. Velldal
(2003) describes a project where a set of Norwegian nouns are grouped into semantic classes
based on their distribution over a large body of text. A word’s distribution in different contexts
is represented as a feature vector in a semantic space model. In his project, Velldal addresses the
problem of a lacking parser for Norwegian by stating that there does not exist any syntactic
parser for Norwegian. Instead, he uses a shallow processing tool on a tagged corpus. The
processing tool “translates” the tagged structures into predicate-argument structures, overcoming
the need for a parser by only analysing those parts of the text relevant for the extraction of the
needed structures. As has been explained in section 3.2, an extraction method that is based on
surface structures and does not take semantic relations into account, might produce results that
are unsuitable both for subsequent use in anaphora resolution and for generalisation of concepts.
In view of this, the present work has aimed at developing an extraction method that uses parsed
text to collect the meaning structures from the text.
Although it is true that there does not exist any parser that fully covers the Norwegian language
at the moment, there are a few alternative parsers available. Even if these grammars are not
entirely robust enough to return parses on randomly chosen texts, they can be used for the
experiments outlined in this project. The extraction method described in this thesis implements
one of the existing parsing tools for Norwegian bokmål, NorGram (NorGram 2004).
Since there are no easy-to-use automated tools available for use in the extraction process,
obtaining the EPAS from the text involved a substantial amount of manual work, even when
using a parser to automate the extraction. Parsing the texts was definitely of value, though, since
once the texts were parsed and there was a syntactic analysis to work on, the EPAS could more
readily be extracted. Because of the modular nature of the extraction method, the extraction
process is not parser-dependent. Should a new and more robust grammar become available, the
extraction method can be modified to accommodate this. The next section of this chapter briefly
describes how the NorGram/XLE parser was used in the project, while section 3.3.2 describes in
greater detail how the EPAS were extracted from the parser’s output.
42

3.3.1 NorGram in outline
Norsk komputasjonell grammatikk (NorGram) is a computational grammar for Norwegian
bokmål. NorGram is based on the unification-based grammar formalism Lexical Functional
Grammar (LFG), where language is described by means of feature structures that can be
combined in the process of unification. Researchers involved in the NorGram project cooperate
with researchers at Palo Alto Research Center (PARC), former Xerox PARC, who have
developed a well functioning platform for the development of large-scale computational
grammars. This system is called Xerox Linguistic Environment (XLE) and uses LFG as its
theoretical linguistic framework. As such, NorGram can be said to be an LFG formalism for
Norwegian, while XLE is an implementation of LFG.
The NorGram grammar combined with an XLE-module is a relatively broad parser that can
analyse most structures found in Norwegian. It was chosen for the purposes of this project
because it was likely to return successful parse trees of a large part of the sentences found in the
text collections. NorGram’s lexicon is quite large and includes entries of most regular
Norwegian words. One problem with the lexicon with regards to the text collections used for
this project, is that it contains relatively few compounds. All theme-specific texts feature a
theme-specific vocabulary, sometimes with words (especially compound nouns) that cannot be
expected to be found in ordinary dictionaries. This was also the case for the text collection in
this project. Compounded nouns represented the largest group of words added to the lexicon. In
Norwegian, one stands fairly free to form compounds consisting of words that also can exist
individually and have an individual meaning. Whereas in English such compounds are written in
two separate words, for example police investigator, they together form a new noun in
Norwegian, for example politietterforsker (police investigator). This opens for a potentially
infinite class of nouns and makes it virtually impossible to include all possible words in any
lexicon.
The NorGram lexicon was extended in order to be used as a tool to extract the EPAS from the
text collection. Compounds and proper nouns that were part of sentences to be analysed were
added to the lexicon files. To ensure that all EPAS could successfully be extracted, all sentences
that were not parsed were examined to identify the word that represented the problem.
Subsequently, that word was added to the lexicon. A more elegant way to solve the compound
issue would be to make use of a module that splits compounds into the individual words they
43

consist of, or to make use of a component that predicts the part of speech of an unknown word.
One solution that would have been suitable for the purposes of the present work would be to
assume that all unknown words were nouns. However, due to the small size of the corpus used
for this project, none of these strategies were implemented, and unknown words were added to
the lexicon by hand.
When parsing texts with NorGram and XLE, the user has several choices with regards to the
format of the final syntactic analysis. For example, it is possible to receive partial parses, or to
let the system return all the potential analyses of the input sentence. For the purposes of this
project, I received full parses of each sentence in the text material and chose to manually check
each instance where the system returned multiple valid parses and actively decided on the
correct one that I wished to extract the EPAS from.
3.3.2 Extracting EPAS from NorGram
The output provided by XLE upon a successful parse using the NorGram grammar is
particularly useful for a subsequent extraction of EPAS. NorGram is based on the LFG grammar
formalism and produces constituent structures (c-structures), functional structures (f-structures)
and minimal recursion semantics structures (MRS-structures) upon parsing a sentence. Each of
these outputs can be useful for a subsequent extraction of predicates and their arguments.
The c-structure in LFG is an external structure which displays an ordered representation of the
words in a sentence or phrase (Bresnan 2001, p. 44). In XLE, the c-structure is represented by a
phrase structure tree, where the terminal nodes are fully inflected word forms. F-structures
represent the internal structure of a sentence. On this level, the “syntactic functions are
associated with semantic predicate argument relations” (Bresnan 2001, p. 45). C-structures and
f-structures are different structures, but display parallel information. Figure 3 below shows the
graphical representation of the c- and f-structures for the sentence Politiet leter etter morderen
(The police is looking for the murderer) generated by NorGram.
44

Figure 3
The most useful structure for the purpose of extracting EPAS from the parse output, is the MRS
structure. In comparison to the c- and f-structures, which are more syntactically motivated, the
MRS structure displays the semantic structure within a sentence. In the next section, this
structure is described in greater detail.
3.3.2.1 Minimal Recursion Semantics
Minimal Recursion Semantics (MRS), developed by Copestake et al. (2003), is a framework for
computational semantics, providing a meta-language for describing semantic structures. The
concept of MRS is primarily semantically motivated and aims at preserving the semantic
structures in the input sentence. MRS allows for expressive adequacy, ensuring that the
linguistic meanings conveyed by a sentence are expressed correctly in the semantic structure.
The primary unit within the framework of MRS is the elementary predication (EP). An EP is a
single relation with associated arguments and will generally speaking correspond to a lexeme
with its argument roles filled. Since MRS provides a “flat” representation where the EPs are
never nested within each other, semantically irrelevant implicit information about the syntactic
structure of a phrase is avoided. The simple principle is that each EP has a “handle” which
identifies it as belonging to a particular tree node and argument positions in EPs can be filled
with handles which correspond to the EPs that belong immediately under it in the tree structure.
45

More than one EP with the same handle entails that the EPs are conjoined and on the same node
in the structure. Tree structure in this sense do not refer to the c-structure, but to an abstract
structure which shows the hierarchical representation of the EPs.
MRS is implemented in NorGram and the MRS structures provided there are the most
convenient of the output structures from the point of view of EPAS extraction. A sentence or
phrase can contain more than one EPAS, and all predicates with belonging arguments are
displayed in the MRS representation. The MRS structures NorGram displays following the
successful parse of a sentence, contain all the information needed to extract the EPAS. However,
because of the manner in which they are displayed in the XLE graphical interface, it is not
straightforward and simple for a human to see which arguments belong where and as such
identify the EPAS directly. Only by tracing each individual EP and finding corresponding values
in other EPs, is it possible to extract the sentence’s EPAS. Figure 4 below shows the graphical
representation of the MRS structure for the sentence Politiet leter etter morderen (The police are
looking for the murderer).
Figure 4
46

3.4 Altering the source
As already mentioned, parsing randomly selected Norwegian texts is not an entirely
straightforward task. Although NorGram provides for a quite broad grammar, not all linguistic
constructions are parsed and, more importantly, not all words are covered in the lexicon. Ideally,
it would be desirable to collect a limited domain treebank consisting of parsed sentences of the
original texts as I found them on the internet. In practice, this was not a feasible task. It early
became evident that the texts to be analyzed would have to be simplified for practical reasons.
For the purpose of classification, I needed to extract the EPAS present in the texts. All the other
information that was included in every sentence was not essential or necessary for the project.
Although aware that it would be more scientific, and in any respect better, to extract the EPAS
from original texts that have not been tampered with by me, this was not possible within the
framework of this thesis. Given that I would have to simplify the texts in any case, I decided to
cut most information that was irrelevant for the extraction of the (most central) EPAS. This
process was performed on alle sentences in the text collection. Mainly adverbial phrases were
excluded, on the basis that they would not be included in the extracted EPAS in any case. The
examples in (3-11) below illustrate a typical example:
(3- 11)
a. Original sentence:
Etter at hun ble funnet opplyste et vitne at hun hadde hørt
høye rop om hjelp fra stedet tidlig søndag morgen.
After she was found a witness informed that she had heard loud screams for
help from the area early Sunday morning.
b. Simplified form:
Et vitne opplyste at hun hadde hørt høye rop.
A witness informed that she had heard loud screams.
c. Extracted structures:
høre,vitne,rop
høy,rop,?
opplyse,vitne,?
hear, witness, scream
loud, scream,?
inform, witness,?
47

The pre-editing of the text collection will naturally have affected the resulting EPAS list. All
structures from the original texts are not extracted and the EPAS list as a consequence does not
include all relevant context patterns for the domain. Still, for the purposes of a pilot study such
as in this thesis, the central structures, which display the most typical context patterns for the
domain, include enough information to provide a tendency of the usefulness of the method. For
the purpose of subsequent analyses, the extraction process can easily be performed on unedited
original texts.
3.5 Finding the words
The process of extracting meaning structures such as the EPAS from the texts in the text
collection is a substantial undertaking. It is also quite a tedious task, and since tedious tasks tend
to benefit from being automated I wrote the Perl script Ekstraktor, which interprets the MRS-
structures of a sentence and thereby puts together the EPAS for each parsed sentence. This
chapter describes the outline of the automated extraction process.
XLE provides the user with the choice of several output formats, including a graphical user
interface that displays a tree graph of the parse as well as its F-structure and MRS-structure. By
choice, the output can also be viewed as a file of Prolog predicates. In the process of extracting
the EPAS, Ekstraktor reads the Prolog output, saves relevant information in a system of arrays,
and subsequently performs several tests and actions on the stored information in order to present
a list of all EPAS found in the parsed sentence.
The MRS structures as represented in the Prolog output, provides all the information needed to
extract the EPAS. Initially, the main EP with belonging arguments must be found. Since for the
purpose of this paper, the linguistic structures analyzed are limited to full sentences, the main EP
must display the category ‘v’ for verb. Once the main EP is identified, the semantic values for it
and for its belonging arguments must be found. Subsequently, all the remaining predicateargument
structures must be found. For them, there is no restriction as to which category they
have. Consider the sentence shown in (3-12) together with an extract of the Prolog output of the
parse shown in (3-13):
48

(3- 12)
(3- 13)
Politiet leter etter morderen
The police are looking for the murderer
cf(1,eq(attr(var(19),'ARG0'),var(20))),
cf(1,eq(attr(var(19),'ARG1'),var(21))),
cf(1,eq(attr(var(19),'ARG2'),var(22))),
cf(1,eq(attr(var(19),'LBL'),var(10))),
cf(1,eq(attr(var(19),'LNK'),14)),
cf(1,eq(attr(var(19),'_CAT'),'p')),
cf(1,eq(attr(var(19),'_CATSUFF'),'sel')),
cf(1,eq(attr(var(19),'relation'),semform('etter',15,[],[]))),
cf(1,eq(attr(var(20),'type'),'event')),
cf(1,eq(attr(var(21),'PERF'),'-')),
cf(1,eq(attr(var(21),'TENSE'),'pres')),
cf(1,eq(attr(var(21),'type'),'event')),
cf(1,eq(attr(var(22),'NUM'),'sg')),
cf(1,eq(attr(var(22),'PERS'),'3')),
cf(1,eq(attr(var(22),'type'),'ref-ind')),
cf(1,eq(attr(var(23),'ARG0'),var(21))),
cf(1,eq(attr(var(23),'ARG1'),var(24))),
cf(1,eq(attr(var(23),'ARG2'),var(22))),
cf(1,eq(attr(var(23),'LBL'),var(10))),
cf(1,eq(attr(var(23),'LNK'),10)),
cf(1,eq(attr(var(23),'_CAT'),'v')),
cf(1,eq(attr(var(23),'_PRT'),'etter')),
cf(1,eq(attr(var(23),'relation'),semform('lete',11,[],[]))),
cf(1,eq(attr(var(24),'NUM'),'sg')),
cf(1,eq(attr(var(24),'PERS'),'3')),
cf(1,eq(attr(var(24),'type'),'ref-ind')),
cf(1,eq(attr(var(25),'ARG0'),var(22))),
cf(1,eq(attr(var(25),'BODY'),var(26))),
cf(1,eq(attr(var(25),'LBL'),var(27))),
cf(1,eq(attr(var(25),'LNK'),18)),
cf(1,eq(attr(var(25),'RSTR'),var(14))),
cf(1,eq(attr(var(25),'relation'),semform('def',31,[],[]))),
cf(1,eq(attr(var(26),'type'),'handle')),
cf(1,eq(attr(var(27),'type'),'handle')),
cf(1,eq(attr(var(28),'ARG0'),var(24))),
cf(1,eq(attr(var(28),'BODY'),var(29))),
cf(1,eq(attr(var(28),'LBL'),var(30))),
cf(1,eq(attr(var(28),'LNK'),0)),
cf(1,eq(attr(var(28),'RSTR'),var(17))),
cf(1,eq(attr(var(28),'relation'),semform('def',9,[],[]))),
cf(1,eq(attr(var(29),'type'),'handle')),
cf(1,eq(attr(var(30),'type'),'handle')),
cf(1,eq(attr(var(31),'ARG0'),var(22))),
cf(1,eq(attr(var(31),'LBL'),var(13))),
cf(1,eq(attr(var(31),'LNK'),18)),
cf(1,eq(attr(var(31),'_CAT'),'n')),
cf(1,eq(attr(var(31),'relation'),semform('morder',19,[],[]))),
cf(1,eq(attr(var(32),'ARG0'),var(24))),
cf(1,eq(attr(var(32),'LBL'),var(16))),
cf(1,eq(attr(var(32),'LNK'),0)),
cf(1,eq(attr(var(32),'_CAT'),'n')),
cf(1,eq(attr(var(32),'relation'),semform('politi1',1,[],[]))),
49

The Prolog code extract in (3-13) shows the MRS representation of the sentence in (3-12) by
listing all the EPs in the sentence as well as the relationships that hold between the individual
EPs. In simplified terms, the value of the attribute ‘semform’ holds the semantic form of the
predicate, and the values of ‘ARG1’ and ‘ARG2’ point to the EPs where the semantic forms for
argument 1 and argument 2 can be found. In order to extract all EPAS from such a Prolog file,
one must go through all the EPs in turn, and find the semantic forms of each main EP and its
belonging argument 1 and argument 2. In the extraction process, this matching and tracing of
values is performed by the script Ekstraktor.
The algorithm behind Ekstraktor is divided into two more or less separate parts: information
retrieval from the Prolog file and processing of the information that was found and stored. Perl
was chosen as the programming language mainly because of its excellent pattern matching
facilities. Perl offers a very powerful and flexible regular expression syntax which lets the
programmer construct regular expressions that will handle all kinds of pattern matching. For the
information retrieval part of Ekstraktor, it was desirable to go through an input file, check for
various patterns and store parts of the input file relevant to how the patterns were matched. (3-
14) shows one of the pattern checks in Ekstraktor – if the line read from the file contains the
string:
‘relation’),semform(
the entire line is stored in the array semform.
(3- 14)
if ($linjeFraFil =~ m/'relation'\),semform\(/){
push(@semform, $linjeFraFil);
}
By going through the input file line by line and checking for several patterns, all information
relevant to extracting the EPAS is stored in a system of arrays. To be able to keep track of which
EP the various values belong to, a system of two arrays for each argument type is used – one for
EP number and one for argument value. The ARG0 arrays correspond to the predicates in the
structures and for each, the semantic form can directly be found in the semform-array. The
50

ARG1 and ARG2 arrays display a value that must be traced before the semantic form can be
extracted. A simplified example of the argument arrays for the sentence in (3-12) is shown in
(3-15):
(3- 15)
ARG0:
EP VALUE
23 21
25 22
28 24
31 22
32 24
ARG1:
EP VALUE
19 21
23 24
ARG2:
EP VALUE
19 22
23 22
To find the EPAS for this sentence, the first EP in the ARG0-array is incorporated in a regular
expression which then is used for pattern matching in the members of the semform-array. If
there exists an entry which matches the pattern, that is, which has an EP-value identical to the
first EP in the ARG0-array, the semantic form is retrieved. To find the belonging arguments 1
and 2, the ARG1 and ARG2-arrays are searched for an EP identical to the one of the predicate.
If such an EP is found, the corresponding value is retrieved – for ARG1 in our example that
would be the value 24. To find the semantic form of this value, we must find the EP where this
value is identical to the value of ARG0, that is, the ARG0-array must again be consulted. When
the EP is found, the semform-array can be pattern matched and the semantic form can be
retrieved. To retrace the example; following such a procedure, the sentence in example (3-16):
(3- 16)
Politiet leter etter morderen
The police are looking for the murderer
is represented with the following EPAS, extracted from the Prolog file of the parse:
(3- 17)
lete-etter,politi,morder
look-for,police,murderer
For a detailed walkthrough of Ekstraktor, please consult Appendix A. The program code is
available in Appendix B.
51

3.6 Evaluation of the data set
The data set created by the extraction process consisted of 195 elementary predicate-argument
structures in its raw form. The original EPAS list was not directly applicable for the next parts of
the project. Not all of the extracted structures on the list were suitable for further analysis. Some
of the EPAS were not given an optimal analysis (for my purposes) by the grammar, some were
irrelevant for the later analysis and some were not extracted correctly from the MRS by the Perl
script. The dataset was post-edited to achieve a set of EPAS that did not include erroneously
extracted or undesired structures. With such a small collection of structures as is the case in this
project, the inclusion of only a few incorrect structures would be likely to skew the subsequent
analysis and possibly produce false results.
In the following, I will briefly outline some of the reasons why the EPAS list included incorrect
structures and describe how the list was revised.
3.6.1 Errors from the grammar
Some of the undesired structures in the original EPAS list were directly caused by
characteristics in the NorGram grammar. In the original EPAS list, there were for instance
several structures of the type exemplified by (3-18):
(3- 18)
a. verbal predicate, nominal argument
b. preposition, verbal predicate, nominal argument
These structures should preferably have been combined into one EPAS. The example in (3-19)
below shows a concrete instance from the EPAS list and is analogous to several other instances:
(3- 19)
a. bo, Anne live, Anne
b. i, bo, studentkollektiv in, live, student housing
The structure is extracted from the following sentence from the text material:
52

(3- 20)
Anne Slåtten bodde i et studentkollektiv utenfor Førde sentrum.
Anne Slåtten lived in student housing outside central Førde.
As example (3-20) shows, these structures originate from sentences featuring a verb with an
adverbial complement. The adverbial is realised as a prepositional phrase where the preposition
is selected by the verb. It would have been expected that sentences such as Anne bodde i et
studentkollektiv (Anne lived in student housing) would result in one EPAS with the entity
studentkollektiv somehow realized as the structure’s argument 2. Instead, the MRS structure of
this and other similar sentences did not provide the necessary link between the verb as predicate
and studentkollektiv as the second argument. When discussing this problem with the developers
of the grammar, the source of the obstacle was easily identified. In the grammar, the verb bo
(live) existed as an intransitive verb, not allowing for an adverbial complement to be analysed as
required to produce the desired EPAS. In order to allow for this and similar sentences with the
verb bo to produce one EPAS with the correct relationship between the predicate and its
arguments, the entry for bo was altered. A solution which allows for an arbitrary preposition was
favoured, instead of creating a new template that specifies the possible following prepositions.
Analysing the sentence above with the revised grammar produces structures of the following
type:
(3- 21)
bo, Anne, studentkollektiv
live, Anne, studenthousing
The same phenomena was observed for a few other verbs with prepositional phrases as
complements, such as gjemme i (hide in) and observere i (observe in). In these instances, the
structures were manually edited.
3.6.2 Irrelevant structures
Some of the structures that were extracted correctly from the text collection, were simply
removed in the final post-editing of the EPAS list. These structures were not directly relevant for
the later analysis process and would not contribute with any valuable information for the
53

eferent-guessing procedures. In total 22 such structures were removed. The majority of these
structures originate from adverbial phrases in the text collections. Location and temporal
adverbs, featuring as prepositional phrases in the texts, show up in the EPAS list as structures
disjoint from the rest of the sentence, not unlike the structures mentioned above. The preposition
functions as the EPAS’ predicate, resulting in structures of this type:
(3- 22)
på, funn, åsted
on, finding, crime scene
Such structures were left out of the final EPAS list.
Another type of correctly extracted structure that was omitted from the final list, were structures
particular to the information structure in the grammatical analysis. The extraction script returned
all predicate-argument structures present in the MRS structure for each parsed sentence. This
resulted in a few structures that did not hold information that it was desirable to maintain in the
EPAS list. Below is an example of such a structure:
(3- 23)
unspec_loc, , place
3.6.3 Manually added structures
Not all the predicate-argument structures present in the text collection were successfully
extracted by means of the extraction method. After removing unwanted structures from the
EPAS list, the texts in the text collection were gone through manually to gather any EPAS that
were not returned by the automated extraction process. Had the text collection been larger so
that the list of automatically extracted EPAS had been correspondingly bigger, too, this may not
have been a necessary action. In the case of a (substantially) larger EPAS list, the structures that
were not collected in the extraction process could have been abstained from, as the structures
that would have been extracted would have provided enough information for the subsequent
classifications and analyses. As is the case in this project, though, the text collection and the
resulting EPAS list are very small. All information that can be extracted from the texts is of
54

value and highly desirable. As such, it was a logical next step following the removal of
unwanted structures, to make sure that all desirable structures had been collected from the texts.
Several structures were added; many of which had been subjected to only partial extraction in
the extraction process. This may in part be due to the syntactic analysis and in part to the
matching by the Perl script. Further, EPAS were manually extracted from one additional text
that had not been parsed, and therefore not been part of the initial extraction process. In total,
this yielded 74 additional EPAS. The example in (3-24) below provides an example of a
manually edited EPAS. (3-24a) shows the EPAS as it was after the automatic extraction process.
While going through the texts, it became clear that this EPAS had not been extracted in a way
that represented the meaning in the sentence it originated from, and therefore did not have an
optimal structure. The EPAS was therefore manually modified to the form shown in (3-24b).
(3- 24)
a. Original EPAS:
ta, syklist, kontakt
make, biker, contact
b. Manually corrected EPAS:
ta-kontakt-med, syklist, politi
make-contact-with, biker, police
Appendix C contains the EPAS list, while Appendix D shows the alignment between sentences
in the text and the extracted EPAS.
3.6.4 Comments about the EPAS list
The revised EPAS list consists of 223 elementary predicate-argument structures. 24 structures
have been modified as described above, and 74 have been added. The list contains most EPAS
present in the text collection and represents a list of verb-subject-object relations found within a
limited thematic domain. While it is clear that the list could have been expanded by adding
further texts to the collection, it was not possible to extend the list further within the frameworks
of this project. The EPAS list is large and varied enough to show a tendency. Certainly, the
instances of individual EPAS would have been higher and the list would also have been
55

enriched by several new EPAS. Still, for the purposes of this thesis, the list includes a broad
enough variety of structures to be of use in the classification phase.
In the process of assessing the quality of the EPAS list, it became evident that the most
interesting structures are the simplest ones. The EPAS corresponding to verb-subject-object
relations are the ones that contribute with most information about the selectional restrictions of
the domain. An alternative way to obtain an effective and robust extraction of EPAS might have
been to concentrate only on this type of structure, rather that focusing on extracting all EPAS
from the text collections and then filtering out unwanted ones.
In order to estimate the potential of a classification of the EPAS list, line diagrams were created
using Formal Concept Analysis (FCA). FCA is a methodology of data analysis and knowledge
representation which identifies conceptual structures in data sets, and was a useful tool in the
process of identifying how the predicates and arguments in the EPAS list related to each other.
FCA distinguishes between two types of elements; formal objects and formal attributes. A
formal concept is seen as a unit consisting of all belonging objects and attributes (Wolff 1991, p.
430). Starting with any set of formal objects, all formal attributes the objects have in common
can be identified. When using FCA to structure the data in the EPAS list, the arguments were
termed objects, while the predicates were termed attributes. An FCA line diagram consists of all
objects and attributes in a given context, organised hierachically according to their shared
properties. Figure 5 below shows the FCA line diagram for part of the structures in the EPAS
list 4 . Each white label corresponding to an argument from the EPAS list should be understood as
a concept, and information about each concept can be read by following the upward leading
paths from each concept. An object has a given attribute if there is an upward leading path from
the object to the attribute (Wolff 1994, p. 431). Using the arguments/formal objects lensmann
(sergeant) and Fonn as a starting point, the associated predicates/formal attributes gi (give), and
bede-om (ask-for) can be identified. The arguments lensmann and Fonn co-occur with the
predicates gi and bede-om, while politi (police), which is further down in the hierarchy, cooccurs
with other predicates as well as those higher up in the diagram (gi, bede-om and bekrefte
(confirm)). In other words, more general concepts are found toward the bottom of the diagram,
while specialised concepts are found by following the paths upwards. For the data material in
4 The diagram was made using the program Concept Explorer, downloadable from
http://sourceforge.net/projects/conexp
56

this project, this can be interpreted in terms of the contextual distribution the arguments have.
Arguments found in the lower parts of the diagram are more general and co-occur with a wider
range of predicates than the arguments found higher up in the hierarchy. In Figure 5, it can be
seen that gjerningsmann (perpetrator) and drapsmann (killer) have similar distributions in the
data material; drapsmann co-occurs with the predicates velge (choose) and gjemme (hide), while
gjerningsmann only is found in connection with gjemme. On the basis of the formal concept
analysis, it is clear that the EPAS list contains several arguments which show a distribution
particular to their semantic meaning. The different lines in the diagram show interesting bundles
of semantically related arguments and confirm the assumption that different types of arguments
show different contextual distribution within the thematic domain.
Figure 5
57

4 Classification
In order to use the structures in the EPAS list as an aid in anaphora resolution, they have to be
processed. The pre-processing in section 3.6.4 has shown that there does exist interesting
distributions in the data set and indicates that certain groups of arguments display distributions
particular for the domain. As a step toward exploring if these distributions can be used to
represent selectional restrictions and thus function as real-world knowledge for the domain, the
words in the EPAS list must be classified. This procedure uses the context pattens that a word
occurs in to classify the word, for example allowing for an argument to be classified according
to the predicates it co-occurs with. A classification of this type gives information about which
word to expect in a given context pattern and the results can therefore be used in the process of
chosing the most likely antecedent for an anaphor. In this respect, the most likely antecedent
must be interpreted as the most likely antecedent given a particular contextual pattern.
In the following, the EPAS list will first be classified to see if the context patterns represented
by the EPAS contain enough information to suggest the correct antecedent for anaphoric
expressions from the text collection. Then an association of concepts will be performed, creating
bundles of those arguments which occur in similar contexts/with similar predicates. These
concepts will then be applied in co-occurrence with the classification method to see if they
improve the process of suggesting the correct antecedent for the anaphors.
For the purposes of classification and testing, the EPAS list was divided into training and test
sets. The test set consist of all structures containing pronouns, while the training set consists of
the remaining EPAS. In the case of the test set, the correct antecedent for each pronoun was
identified manually and added to the test file. When testing with the test instances, the classifier
assigns an antecedent based on the patterns it has seen in the training set. In this way, the correct
antecedent in each test case functions as a means of measuring the success rate of the
classification. The test set provides a good way of testing the product of the classification and
gives a measure as to whether the correct antecedent can be assigned based on training on
occurrences of EPAS/context patterns.
58

The process of classifying the constituents of the EPAS is most useful if the aim of the
classification is held clearly in mind. Classifying arguments relative to the predicates and the
other arguments they co-occur with can give information about two things;
• is the data set generalisable enough to allow inference of the single correct antecedent in
each test case?
• is the data set generalisable enough to allow inference of words within the semantic
concept group that the correct antecedent belongs to?
In this thesis, it is of interest to identify all the words which occur in specific environments. As a
reaction, we are interested in finding the members which can co-occur in a specific pattern – and
not necessarily only in the single correct antecedent.
The classification phase in the present work has three steps; firstly classification through a
memory-based learning algorithm, secondly association of semantic classes from the text
material by looking at contextual environments, and thirdly classification through application of
the concept groups gathered in step two. In the following, the classification method will be
described in more detail.
4.1 Step I: Classification with TiMBL
TiMBL (Tilburg Memory Based Learner) (Daelemans et al. 2003) is a memory-based learning
(MBL) tool developed by the ILK research group at the University of Tilburg (ILK 2004).
TiMBL has been developed with the domain of NLP specifically in mind and provides an
implementation of several MBL algorithms.
Within MBL, or lazy learning (Daelemans et al. 1999), training instances are simply stored in
memory. Upon encountering new instances, classification is performed by comparing the new
instance to the stored experiences and estimating the similarity of the new instance to the old
ones. The stored example(s) most similar to the new instance is picked as its classification. This
approach stands in opposition to rule-induction based methods, which also are called greedy
algorithms. In greedy learning algorithms, the learning material is used to create a model with
expected characteristics for each category to be learned. Daelemans et al. (1999) show that
59

language processing tasks tend to benefit from lazy learning methods, particularly because the
individual examples in the training material are not abstracted away from in the process of
creating rules. When a new data instance is classified, it is compared to all previously seen
examples, including low-frequent ones. This suggests that in the case of relatively small data
sets, such as the one in the present work, MBL tools are particularly suitable.
By consulting previously seen data and estimating the similarity between old and new instances
of data, MBL algorithms such as TiMBL are able to calculate the likelihood of new instances of
data. This is done by creating a classifier which essentially consists of an example set of
particular patterns together with their associated categories. The classifier can subsequently
classify unknown input patterns by applying algorithms to calculate the similarity, or distance,
to the known patterns stored in memory. The Nearest Neighbor approach is one commonly used
means to estimate this distance and is described in more detail in the following section.
4.1.1 The Nearest Neighbor approach
Daelemans et al. (2003, p. 19) state that all MBL approaches are founded on the classical k-
Nearest Neighbor (k-NN) method of classification (Cover and Hart 1967). This approach
classifies patterns of numeric data by using information gained from examining and classifying
pattern distributions observed in a data collection. In the k-NN algorithm, a new instance of data
is classified as nearest to a set of previously classified points. The intuition is that observations
which are close together will have categories which are close together. When classifying a new
instance of data, the k-NN approach weights the known information about the closest similar
data instances most heavily. In other words, a new instance of data is classified in the category
of its nearest neighbour. In large samples, this rule can be modified to classifying according to
the majority of the nearest neighbours, rather than just using the single nearest neighbour. The
k-NN approach has several implementations in TiMBL. As TiMBL is designed to classify
linguistic patterns, which in most cases consist of discrete data values and allow for a large
number of attributes with varying relevance, the k-NN algorithm is not used directly. Instead,
the classification of discrete data is made possible through a modified version of the k-NN
approach, as well as other algorithms.
60

There are several different distance metrics incorporated in TiMBL and, as will be described
later, the user can choose the one that suits the data material best. The basic metric is the
Overlap Metric, where the distance between two patterns is calculated as the sum of differences
between the features of the two patterns (Daelemans et al. 2003, p. 20). The algorithm
combining the k-NN approach with the overlap metric within TiMBL is called IB1 (Aha, Kibler
and Albert 1991, in Daelemans et al. 2003). In this algorithm the value of k is the number of
nearest distances (usually 1), allowing for a nearest neighbour set which may comprise several
instances which all share the same distance to the a test example. The IB1 algorithm finds the k
nearest neighbours of a test case by calculating the distance between a test instance Y and a
training instance X. The distance between the two instances is the sum of the distances between
the instances’ different features. If k = 1, a test instance is assigned the category of its single
nearest neighbour. In cases where the algorithm finds a set of nearest neighbours, the majority
vote of the set is chosen. This implies a certain bias toward high frequent categories, which in
many cases will hold the majority vote.
4.1.2 Testing
To create a classifier, TiMBL needs training and test data in a feature vector where each
instance consists of a fixed length of feature values followed by a category. For testing purposes,
the feature sequence is used when the distance between a test instance and the training data is
calculated, and the category functions as a means to evaluate whether the assigned classification
was valid. Because the test data is compared directly with the training data, separate training and
test sets are needed. In this project, the EPAS list was split into a training set consisting of all
EPAS without pronouns and a test set consisting of the EPAS with pronouns. In addition, testing
through TiMBL’s leave-one-out option was performed; here testing is done on each pattern of
the training file by treating each pattern in turn as a test case (Daelemans et al. 2003, p. 35).
In the classification phase the alignment of category and description features is stored, so that
the categories of new, unseen sequences of description features can be probabilistically inferred
in the following test phase.
Regardless of the input format chosen, a classification with TiMBL presupposes that the training
material consists of a number of features to be learned from, as well as a predetermined category
61

which is the desired output category. The comma-separated values format was used for the
EPAS classification. In order to classify the constituents of each EPAS based on the contextual
patterns in the structure, each part of the EPAS was classified with reference to the other
constituents in it. Somewhat analogous to the way that a screw can be described as being small,
long and containing no holes, the argument åsted (crime scene) can be described through its cooccurrence
with the predicate ankomme (arrive) and the argument etterforsker (investigator)
(example (4-1)). This makes it possible to train a classifier on the EPAS list, using the argument
which’s environment is to be learned as category label, and each constituent in the EPAS as
features. To avoid that the category was explicitly present in the training material and ensure
that the classifier was trained only on the environment of the desired category, the relevant
feature was ignored using TiMBL’s ignore option. In order to classify the structures once for
each argument type, two different data sets were prepared. Example (4-1) shows the format of
the three-feature dataset that was used. The parentheses indicate that the feature in question was
ignored when training and classifying.
(4- 1)
features category
a. predicate, (argument 1), argument 2 argument 1
b. predicate, argument 1, (argument 2) argument 2
Example (4-2) shows excerpts of the two input files: (4-2a) shows the structures with argument
1 as category, while (4-2b) shows the same structures with argument 2 as the category. The
classifier is given two constituents of an EPAS to learn from and the target constituent is given
as the EPAS’ category.
(4- 2)
a. ankomme,etterforsker,?,etterforsker
ankomme,etterforsker,?,etterforsker
ankomme,etterforsker,åsted,etterforsker
antyde,politi,?,politi
avhøre,?,person,?
avhøre,?,vedkommende,?
avhøre,politi,vitne,politi
62

. ankomme,etterforsker,?,?
ankomme,etterforsker,?,?
ankomme,etterforsker,åsted,åsted
antyde,politi,?,?
avhøre,?,person,person
avhøre,?,vedkommende,vedkommende
avhøre,politi,vitne,vitne
The output file that is created when TiMBL has classified the input data and run a test with the
test data consists of the input given in the test set with the category predicted by TiMBL added
at the end of each line. Further, the output supplied by TiMBL upon a successful training and
testing round gives information about the actions in the various stages of analysis. TiMBL’s
actions can be divided into three separate phases; in phase 1 the training data is analysed, in
phase 2 the items in the training data are stored for efficient use during testing and in phase 3 the
trained classifier is applied to the test set. For the purposes of the EPAS analysis, the default
algorithm was used in the test phase. This algorithm computes the similarity between a test and
a training item in terms of weighted overlap; the total difference between two patterns is the sum
of relevance weights of those features which are not equal (Daelemans et al. 2003, p. 13).
The classification of the EPAS and the subsequent testing was carried out in two distinct steps;
classification and testing of argument 1 and argument 2 was done separately. The results of the
classification and testing is described in the following sections.
4.1.2.1 Classifying argument 1
Several experiments were run through TiMBL with the aim of classifying occurrences of
argument 1 according to the environment they occur in. The classifier was trained on all EPAS
not containing pronouns and then tested. For the purpose of classifying occurrences of argument
1, an EPAS list with the relevant argument 1 as category label was used. In the following
descriptions of the performed tests, this list will be referred to as EPAS_arg1.
Test 1
Training set: EPAS_arg1 with no pronouns, argument 1 ignored.
Test set: EPAS with pronouns in argument 1 position.
Result: 57,69% (15/26) correct classifications
63

The classifier was created with EPAS_arg1 with no pronouns as training set and tested with all
EPAS containing pronouns in the position of argument 1. For the test set, each EPAS was
completed with the antecedent for its pronoun. For reasons of classification and testing, the
antecedent was appended at the end of each EPAS, thus functioning as the category label for the
structure. In total, there were 26 EPAS with pronouns in the position of argument 1. (4- 3) below
shows an example from the test file with pronouns as argument 1:
(4- 3)
få,pron,rapport,politi
When classifying with argument 1 as category label and testing with EPAS with pronouns as
argument 1, TiMBL assigned the correct category in 57,69% (15/26) of the test cases. One of
the cases where the classifier had assigned the “wrong” category was actually not incorrect, the
antecedent was of a form that did not exist in the training material (antecedent: kvinne/vitne
(woman/witness), assigned category: vitne (witness)). Furthermore, in six of the incorrectly
assigned categories, the category chosen by the classifier was semantically close to the correct
antecedent. Example (4-4) below shows the seven examples where the incorrect categories
assigned by the classifier can in fact be viewed as belonging to the same semantic group, and
thus at least as a partially successful classification. Regarding all these instances as successful
category assignments would heighten the classifier’s correct categorisations to 84,61% (22/26).
(4- 4)
Correct antecedent Assigned category
kvinne/vitne (woman/witness) vitne (witness)
Fonn (Fonn) politi (police)
Kripos-spesialist (Kripos specialist) politi (police)
politimester (police chief) Fonn (Fonn)
politi (police) etterforsker (investigator)
politi (police) Fonn (Fonn)
Slåtten (Slåtten) kvinne (woman)
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Test 2
Training set: EPAS_arg1 with no pronouns, argument 1 ignored.
Test method: leave-one-out
Result: 42,40% (81/191) correct classifications
When training and testing on the EPAS_arg1 list with pronouns removed, the classifier
produced a quite poor accuracy of 42,40%. TiMBL’s leave-one-out option makes it possible to
train and test on the same material, as each pattern in the training file is used as a test case while
the rest of the patterns are used as training material. One reason for the relatively low percentage
of correctly classified instances is most likely the small size of the data set. With only 191
patterns to learn from, the classifier does not have enough diversity in the examples to provide
correct classifications and also does not find enough occurrences of the individual patterns to be
able to pick the correct category. Since politi (police) by far is the most frequent feature in the
EPAS list, many instances are wrongly assigned the category “police” by virtue of the majority
vote of the nearest neighbour classification. An attempt to avoid this effect is described in test 3.
Examining the instances where the classifier assigned the wrong category to an EPAS showed
that in 27 of the incorrectly classified cases, the assigned category was semantically similar to
the correct category. This suggests that the list in itself does contain some relevant information
about the distribution of argument 1 in the data set. Example (4-5) below shows the correct
categories and the categories assigned by the classifier.
(4- 5)
Correct category Assigned category
Anne kvinne (woman)
Slåtten
drapsmann (killer) gjerningsmann (perpetrator)
etterforsker (investigator) politi (police)
Fonn lensmann (deputy)
politi (police)
gjerningsmann (perpetrator) person (person)
Kripos-spesialist(Kripos specialist) politi (police)
kvinne (woman) 23-åring (23-year-old)
65

lensmann (deputy) Fonn
politi (police)
medarbeider (co-worker) politi (police)
person (person) gjerningsmann (perpetrator)
politi (police) lensmann (deputy)
etterforsker (investigator)
politimester (chief of police) politi (police)
polititjenestefolk (police workers) politi (police)
Slåtten Anne
kvinne (woman)
tekniker (technician) politi (police)
23-åring (23-year-old) kvinne (woman)
Test 3
When using the overlap metric, all feature values are seen as equally dissimilar (Daelemans et
al. 2003, p. 23). This means that the classifier is unable to determine the similarity of values
such as politi (police), etterforsker (investigator) and politimester (chief of police) by means of
looking at their co-occurrence with target classes. By using the Modified Value Difference
Metric (MVDM), the features are weighted according to the patterns they occur in.
Unfortunately, MVDM does not perform so well when used on small data sets with values that
only occur a few times in the data set. When trained and tested on the EPAS_arg1 list, MVDM
produced slightly lower accuracies than in the corresponding test with the overlap metric (see
test 2 above). In practise, this meant that the benefits of MVDM could not be exploited due to
the size of the data material.
Test 4
Training set: EPAS_arg1 excluding structures with pronouns and structures with non-verbal
predicates
Test method: leave-one-out
Result: 45,03% (68/151) correct classifications
66

The training and test material was modified by excluding all EPAS with non-verbal predicates,
as well as all EPAS with the predicate være (be). This was done to see if these structures disturb
the data material by adding irrelevant information that does not contribute to giving more
information about the distribution of arguments in the EPAS. The accuracy increased slightly
upon this modification of the data set. The editing did, however, not increase the accuracy when
training on the edited EPAS_arg1 list and testing on EPAS containing pronouns in argument 1
position.
4.1.2.2 Classifying argument 2
Analogous to the classification steps performed for argument 1, the classifications were repeated
for occurrences of argument 2. The EPAS list with the second argument as category label will in
the following be referred to as EPAS_arg2.
Test 1
Training set: EPAS_arg2 with no pronouns, argument 2 ignored.
Test set: EPAS with pronouns in argument 2 position
Result: (0/6) correct classifications
Training the classifier on the EPAS_arg2 list without pronouns and testing on the EPAS with
pronouns in argument 2 position did not produce any correct classifications. This is likely to in
part be because of the small size of the test data set, as well as the homogenous nature of the test
instances. Five of the wrongly classified instances were in fact of the same type; in all instances,
the classifier had assigned the category kvinne (woman), while the correct antecedent was
Slåtten.
Test 2
Training set: EPAS_arg2 with no pronouns, argument 2 ignored
Test set: leave-one-out
Result: 49,73% (95/191) correct classifications
67

Training and testing the classifier on the EPAS_arg2 list with no pronouns produced an accuracy
of 49,73%. As was the case for the corresponding classification of argument 1, it is likely that
the relatively small dataset is a disadvantage for the classification process.
4.1.3 Comments on the results
The results obtained through classifying the EPAS indicate that the information present in the
EPAS derived from the text collection does provide clues about which word to expect in a
specific position. The accuracy scores obtained by training and testing on the EPAS extracted
from a collection of texts suggest that even a small collection of texts on the same domain
provide information to enable a classification approach based on contextual distribution. In the
tests described above, there was a reoccurring tendency that in a number of the cases where the
wrong category was assigned in the test phase, the assigned category bore some semantic
resemblance to the correct category. This reinforces the initial intuition that similar words are
used in similar environments and that the environment can contribute with clues toward the
semantic meaning of a word.
In the following section, the notion of finding words which are similar to each other by virtue of
occurring in the same environments will be explored further.
4.2 Step II: Association of concept groups
The fundamental idea in this thesis is that words display certain semantic features based solely
on the context they are found in. Therefore, when looking for possible antecedents for an
anaphoric expression, the candidates should not only be weighted according to their cooccurrence
in an identical context pattern in a corpus, but also according to their co-occurrence
with similar context patterns. The assumption that words which occur in identical contexts have
related meanings can be used to retrieve words with similar meanings from the data material.
With a target argument and a target predicate as starting point, the association method goes
through the EPAS list and returns words which occur in similar environments to the target
argument. This association is performed in three steps:
68

• level 0: words which co-occur with the target predicate are returned
• level 1: words which occur in the same context as the target argument are returned
• level 2: words which occur in the same context as the words found in level 1 are returned
Level 0 considers the information that is directly accessible from the EPAS list; with a given
predicate as reference point, the co-occurring arguments are retrieved. Level 1 looks at the other
arguments that occur with the same predicates as the arguments retrieved in the first step.
Finally, level 2 performs the same step once again and looks at the arguments that occur in the
same contexts as the arguments collected in level 1. As a result, bundles of concepts are
produced; each concept class consisting of words that are used in the same textual context, and
therefore are likely to be semantically similar. The following example explains how the
association of argument classes is performed.
Level 0
The association method takes as its starting point a predicate from the EPAS list. For a given
predicate, the first and second arguments are listed. The nominal argument etterforsker
(investigator) occurs as argument 1 of the verbal predicate ankomme (arrive) in the text
collection. (4-6) below shows the EPAS with ankomme as predicate.
(4- 6)
ankomme,etterforsker,?
ankomme,etterforsker,åsted
arrive, investigator, ?
arrive, investigator, crime scene
Level 1
In order to find other nominal arguments that occur in the same context patterns as etterforsker,
we must look at the other EPAS in which etterforsker occurs as argument 1. This yields the
EPAS shown in (4-7) below. For the sake of the concept association, the EPAS corresponding to
adjective-noun relations in the original texts are not considered, and therefore not included in
(4-7).
69

(4- 7)
bistå,etterforsker,lensmann
bistå,etterforsker,politi
ha,etterforsker,observasjon
kontakte,etterforsker,vitne
mene,etterforsker,?
rigge,etterforsker,lyskaster
undersøke,etterforsker,åsted
assist, investigator, deputy
assist, investigator, police
have, investigator, observation
contact, investigator, witness
mean, investigator, ?
build-up, investigator,searchlight
examine, investigator, crime scene
For each of the predicates in (4-7), we want to find the other arguments, in addition to
etterforsker (investigator), which occur in the corpus material as the first argument of the
predicate. Traversing the EPAS list in search of these nominal arguments yields the list
presented in (4-8). Pronouns and empty argument slots are omitted from the association since
they generally occur in too many different context patterns to contribute with relevant
information in this kind of analysis.
(4- 8)
ha,politi,medarbeider
ha,politi,teori
kontakte,politi,vitne
mene,politi,?
undersøke,politi,aktivitet
have,police,co-worker
have,police,theory
contact,police,witness
mean,police,?
examine,police,activity
As can be seen from the EPAS in (4-8), politi (police) is the only other argument which occurs
in the same contexts as etterforsker (investigator). So far, the association tells us that there is a
relationship between the concepts etterforsker (investigator) and politi (police) in the sense that
these words occur in the same environments in the text collection.
Level 2
In order to explore the possibility of further associated concepts, the association method goes
one level further. Basically, the first step of the association is repeated, but with new parameters;
for each of the first arguments in (4-8) we need to know which other words can occur in the
same contextual position. Therefore the EPAS list is again consulted and all the other first
arguments that occur in the same environment as politi (police) are returned. This produces the
list in (4-9).
70

(4- 9)
avklare,obduksjon,?
bede-om,lensmann,assistanse
bede-om,Fonn,
bede-om,lensmann,
bekrefte,lensmann,?
bekrefte,politimester,?
finne,leteaksjon,kvinne
få,kjæreste,telefon
gi,Fonn,opplysning
gi,kamera,indikasjon
gi,lensmann,opplysning
gi,lensmann,opplysning
gi,vitneavhør,indikasjon
ha,etterforsker,observasjon
kjenne,generic-nom,Slåtten
kontakte,etterforsker,vitne
mene,etterforsker,?
tro,lensmann,?
clarify,autopsy,?
ask-for,sergeant,assistance
ask-for,Fonn,?
ask-for,sergeant,?
confirm,sergeant,?
confirm,chief of police,?
find,search party,woman
get,boy/girlfriend,telephone
give,Fonn,information
give,camera,indication
give,sergeant,information
give,sergeant,information
give,interview,indication
have,investigator,observation
know,generic-nom,Slåtten
contact,investigator,witness
mean,investigator,?
believe,sergeant,?
As a step toward disregarding arguments which do not occur often enough in the context in
question to be of significance, the method may be limited to only considering arguments which
occur more than once in the text material. In the case of a larger text collection, a different
method of sorting out low frequent arguments would have to be adopted; for the small data set
in this project, disregarding arguments which only occur once was of use. The steps as outlined
above produces the following associated group of concepts:
Figure 6
etterforsker (investigator)
politi (police)
lensmann (sergeant)
Fonn (Fonn)
71

Intuitively, this is a quite good association of concepts, since all the entities in the grouping
belong to the group law enforcement. If a person were to group nominals from the text
collection into semantically similar concept classes, the grouping in Figure 6 would not be an
unlikely result. The grouping as shown in Figure 6, however, is the result of an association
based on context information from the text itself.
4.2.1 Classify
Manually performing the association method as described above on all the EPAS in the data set
proved to be bordering on the impossible, mainly because it implied consulting the data set
multiple times, each time looking for different values and keeping track of the partial goals in
the process. Based on the method as described above, the Perl script classify was written 5 . In the
following, the algorithm implemented in Classify is outlined in brief.
For each predicate:
1. Level 0:
What is ARG1 and ARG2 in the corpus/EPAS list?
2. Level 1:
For each ARG1 = x that was found in 1:
In connection with which other predicates is ARG1 also= x?
For each of these predicates:
Which other words occur as ARG1?
Produces a list of words which occur in the same contexts as x
3. Level 2:
For each word = y in the list from level 1:
Which other predicates does this word also co-occur with?
For each of these predicates:
Which other words occur as ARG1?
Produces a list of words which occur in the same contexts as y
Same procedure is repeated for ARG2.
5
The algorithm was implemented in Perl by Martin Rasmussen Lie, informatics student at the University of
Bergen.
72

(4-10) below shows the output for the predicate ankomme (arrive) as it is produced by classify.
(4- 10)
NIVÅ0, ARG1 (ankomme): etterforsker x 3
NIVÅ1, ARG1 (etterforsker): ? x 5, politi x 5, pron x 3
NIVÅ2, ARG1 (politi): ? x 17, pron x 9, lensmann x 6, Fonn x 2,
forbipasserende x 1, generic-nom x 1, kamera x 1, kjæreste x 1,
leteaksjon x 1, obduksjon x 1, politimester x 1, syklist x 1,
vitneavhør x 1
NIVÅ2, ARG1 (pron): politi x 7, kvinne x 4, vitne x 4, ? x 2,
Anne x 2, bilfører x 2, Slåtten x 2, syklist x 2, etterforskning x 1,
Fonn x 1, generic-nom x 1, kjæreste x 1, lensmann x 1, lommebok x 1,
rapport x 1, teori x 1
NIVÅ0, ARG2 (ankomme): ? x 2, åsted x 1
NIVÅ1, ARG2 (åsted): aktivitet x 1, hybelhus x 1,
minibankaktiviteter x 1, mobiltelefontrafikk x 1, område x 1,
overvåkningsfilmer x 1
NIVÅ2, ARG2 (aktivitet): minibankaktiviteter x 1,
mobiltelefontrafikk x 1, område x 1, overvåkningsfilmer x 1
NIVÅ2, ARG2 (hybelhus): (Ingen referanser)
NIVÅ2, ARG2 (minibankaktiviteter): aktivitet x 1,
mobiltelefontrafikk x 1, område x 1, overvåkningsfilmer x 1
NIVÅ2, ARG2 (mobiltelefontrafikk): aktivitet x 1,
minibankaktiviteter x 1, område x 1, overvåkningsfilmer x 1
NIVÅ2, ARG2 (område): aktivitet x 1, minibankaktiviteter x 1,
mobiltelefontrafikk x 1, overvåkningsfilmer x 1
NIVÅ2, ARG2 (overvåkningsfilmer): aktivitet x 1,
minibankaktiviteter x 1, mobiltelefontrafikk x 1, område x 1
Please consult Appendix E for the full program code for classify.pl.
4.2.2 Associated concept classes
Running the three-level association of semantic classes performed by using the association
method described above on the EPAS list produced six distinct groupings. These concept groups
are shown in (4-11) below.
(4- 11)
a. POLICE:
etterforsker, politi, lensmann, Fonn
investigator, police, deputy, Fonn
b. WOMAN:
Anne, Slåtten, 23-åring, sykepleiestudent, kvinne, beboer
Anne, Slåtten, 23-year-old, nurse student, woman, inhabitant
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c. PERP:
gjerningsmann, drapsmann
perpetrator, killer
d. PERSON:
person, bilfører, syklist, vedkommende
person, car driver, biker, generic-nom
e. OBSERV:
teori, observasjon
theory, observation
f. PLACE:
studentkollektiv, Førde
student housing, Førde
The classes of words shown in (4-11) form groups of concepts which occur in the same
contextual environments within the thematic domain that the EPAS are extracted from. The
groupings seem to reflect real semantic clusters in the sense that one can easily find a label to
describe each group. For the purpose of the text collection in the present work, these six concept
groups represent six distinct semantic groupings that share many features with respect to pattern
distribution in the data set. With a larger data set to run the concept association on, more concept
groups, and also more members within each group, would have been a likely outcome. The
results of the concept association on the small data set in this project, does, however, suggest the
feasibility of the method, as well as show that frequent patterns in smaller text collections also
work toward capturing interesting concept groupings.
4.3 Step III: Using concept groups in TiMBL
The concept groups which emerged as a result of the association performed in section 4.2 above,
represent clusters of words that occur in similar constellations in the data material. The
emergence of concept groups which intuitively seem to have some semantic resemblance to
each other confirms that the context a word fits into does indeed say something about what the
word means, as per the distributional hypothesis.
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In the introduction to this chapter, it was stated that the aim of classifying the EPAS list is
twofold; on the one hand it is of interest to see to which degree the environments that an
argument occurs in over a collection of texts provide sufficient cues to ensure a correct guess of
which argument can be expected in a specific context, on the other hand it is equally interesting
to see if we through classification can narrow down the set of possible arguments for a specific
context pattern. Through the association technique, six groups of words emerged; the members
of each group sharing the feature that they all tend to occur in the same environments.
Previously, it has been stated that some anaphors need access to information about the world in
order to be resolved. This information can to some extent be represented by the concept groups
associated from the data set. By identifying groups of words which typically occur in the same
textual environment, an intuition about which words to expect in which contexts is captured. In
the event of “difficult” anaphors which depend on world knowledge, an anaphora resolution
system can retrieve potential antecedents from the text, check which concept group an expected
antecedent is likely to belong to and consequently chose the antecedent candidate belonging to
the expected concept group. As a first step of examining the usefulness of concept groups in
combination with anaphora, experiments aiming at enhancing the performance of the classifier
in section 4.1 were performed. These experiments are described in the following section.
4.3.1 Testing
Tests were performed in TiMBL, using the relevant concept group as the category for a feature
pattern. Analogous to the testing in section 4.1.2 above, two separate test sets were prepared,
one for the classification of each argument. In the cases where the relevant argument was a
member of one of the concept groups, the head label of the concept group was used as the
category label in the input data. If the relevant argument did not belong to any concept group,
the argument itself was used as category label, as in the tests in section 4.1.2. Example (4-12)
below shows an excerpt of the input file used for training the classifier for argument 1
classification.
(4- 12)
drepe,gjerningsmann,kvinne,PERP
drept,sykepleiestudent,?,WOMAN
død,sykepleiestudent,?,WOMAN
ekstra,patrulje,?,patrulje
75

The aim of the tests performed in this section was to see if the accuracy of the classifier could be
enhanced by training on a complete context pattern with the appropriate concept group as
category label.
Test 1
Training set: EPAS_arg1 with no pronouns and concept classes as category label, argument 1
ignored.
Test method: leave-one-out
Result: 56,54% (108/191)
In this test, the classifier was trained on two features of the EPAS, ignoring argument 1. This
test is analogous to test 2 in section 4.1.2.1, which had an accuracy of 41,20% correct
classifications. In addition to the 108 correctly classified instances, additional five instances
were assigned categories which are semantically similar to the correct category. This was true
for Kripos-spesialist (Kripos specialist), politimester (chief of police), medarbeider (co-worker)
and polititjenestefolk (police workers), which were all assigned the category POLICE. These
words are not part of the concept group POLICE, but are obviously semantically related to the
members of this concept group. Had these words occurred more frequently in the data material,
they could have been expected to show a distribution allowing for their inclusion in POLICE.
The results of this test suggest that labeling EPAS with concept group labels heightens the
accuracy of the classifier. This is not surprising, given the fact that a higher number of context
patterns/EPAS are labeled with the same category in such an approach, making the generalisable
material larger.
Test 2
Training set: EPAS_arg1 with no pronouns and concept classes as category label.
Test method: leave-one-out
Result: 86,91% (166/191)
This test was performed to see if training the classifier on the entire structure of an EPAS
increases the accuracy of assigning concept labels to the structures. The classifier was trained on
all three features of the EPAS. In this case, the classifier performed with a fairly high accuracy,
assigning the correct category in 166 of 191 cases. It is obviously an advantage that all parts of
76

the EPAS can be used in the classification phase when the category to be assigned is not literally
a part of the structures to be learnt from.
Test 3
Training set: EPAS_arg1 with no pronouns and concept classes as category label, argument 1
ignored.
Test set: EPAS_arg1 with pronouns and concept classes as category label
Result: 76,92% (20/26)
As was the case in the corresponding test in section 4.1.2.1, two of the wrongly assigned
categories were in fact within the same semantic group as the correct category. Regarding these
as correct assignments would heighten the result to 84,61%. As was the case in the previous two
tests, the assigned categories in these cases are too infrequent in the EPAS list to surface in the
associated concept groups.
Test 4
Training set: EPAS_arg2 with no pronouns and concept classes as category label, argument 2
ignored.
Test set: EPAS_arg2 with pronouns and concept classes as category label
Result: 83,33% (5/6)
When training the classifier on the EPAS_arg2 list using concept class labels as categories and
testing on the set of EPAS with pronouns in argument 2 position, the classifier resolved five of
the six test instances correctly. In the corresponding test in section 4.1.2, the classifier did not
assign the correct category in any of the six test cases. We did, however, see that five of the test
instances were assigned categories which were semantically similar to the correct antecedent. In
view of the results in the initial test, it came as no surprise that the classifier performed so much
better when used in connection with the concept class labels.
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4.4 Are concept classes useful for anaphora resolution?
The EPAS list has been processed in different ways in this chapter. The tests which have been
described provide an indication of how context patterns extracted from the text collection can be
used to create expectations of which words (or which type of words) that are likely to occur in a
given contextual environment. These expectations can be used to anticipate which word, or
rather which concept, might be the antecedent for an anaphor. The concept groups which
emerged in the association process are simply classes of semantically related words which tend
to have similar contextual distributions within the domain of the text corpus. In order to indicate
the usefulness of such concept classes in the process of resolving an anaphor, the test set of the
EPAS list (all EPAS containing pronouns) was processed with different methods. In (4-13) the
results of these methods are shown. In addition to the tests in TiMBL described in the above, the
anaphors in the test set were resolved manually using the Lappin and Leass approach as
described in section 2.1.2. For these test, the sentence with the anaphor, as well as the preceding
sentence, was considered in each case. This purely syntactic approach identified the correct
antecedent in 16 of the 32 test instances.
(4- 13)
Method Correct assignments
Syntactic method 50% (16/32)
TiMBL 46.87% (15/32)
TiMBL with concept groups 78,12% (25/32)
The results shown in (4-13) suggest that using concept groups may indeed be a useful approach
in anaphora resolution. Especially in the case of anaphoric expressions where the antecedent is
not clearly stated in the text it may be useful to have an idea of which type of antecedent one
might expect. 10 of the 32 EPAS containing pronouns were of this kind. The syntactic approach
could naturally not resolve these anaphors, as an antecedent not clearly present in the text hardly
can feature on a list of possible candidates. These types of anaphors require real-world or
domain knowledge to be resolved. In the case of 4 of these 10 EPAS, the EPAS list could not be
consulted to find likely antecedents. Because of the small size of the data set, some predicates
only feature once. This was the case for the five predicates jobbe-utfra (work-from), kartlegge
(map), ta (take), varsle (notify) and ville (want) which all only co-occur with pronouns. With the
78

exemption of jobbe-utfra, none of the antecedents in these cases can be predicted on the basis of
the distribution of predicates and arguments in the EPAS list. (4-14) shows the instances where
the EPAS list could be consulted in the process of finding likely antecedents for these anaphors.
In the case of ha (have) and komme-i-kontakt-med (come-into-contact-with), other EPAS with
the same predicates where retrieved from the EPAS list. Since ha and komme-i-kontakt-med
occur in identical or very similar patterns with politi as the first argument, this would be the
preferred candidate for the antecedent in (4-14a), (4-14c) and (4-14d). In the case of (4-14b), the
predicate jobbe-utfra only has this one occurrence in the EPAS list. This means that similar
patterns must be examined in the search for a possible antecedent. By consulting the EPAS list,
it can be found that teori (theory) only occurs as a second argument in connection with politi as
first argument. This would suggest that politi is a potential antecedent for the pronoun. By
applying the concept groups, the list of possible antecedents motivated by the texts can be
expanded to also include the other arguments which have been found to display a similar
distribution to the arguments which actually co-occur with the predicate in question. In the case
of the pronouns in (4-14), politi is the correct antecedent in all of the cases.
(4- 14)
EPAS with
pronoun
similar EPAS antecedents
from list
a. ha,pron,teori ha,etterforsker,observasjon
ha,politi,medarbeider
ha,politi,teori
b. jobbeutfra,pron,teori
ha,politi,teori
forkaste,politi,teori
c. komme-i-kontaktmed,pron,bilfører
d. komme-i-kontaktmed,pron,syklist
komme-i-kontaktmed,politi,bilfører
komme-i-kontaktmed,politi,generic-nomkomme-i-kontaktmed,politi,bilfører
komme-i-kontaktmed,politi,generic-nom
politi
etterforsker
politi
concepts
lensmann
Fonn
79
etterforsker
lensmann
Fonn
politi etterforsker
lensmann
Fonn
politi etterforsker
lensmann
Fonn
The examples in (4-14) indicate how the method described in this thesis can function. In cases
where there is no clearly expressed antecedent in a text, or where the resolution of an antecedent

equires knowledge about the world (or knowledge about how predicates and arguments
combine within a domain), the method can be of aid. Consider again the examples from the
introduction, repeated in (4-15) below:
(4- 15)
a. Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig
kommer til å drepe igjen. Han etterlyser vitner som var i sentrum søndag
kveld.
The sergeant leading the investigation says that the perpetrator probably will
kill again. He puts out a call for witnesses who were in the city centre Sunday
evening.
b. Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig
kommer til å drepe igjen. Han er observert i sentrum.
The sergeant leading the investigation says that the perpetrator probably will
kill again. He is observed in the city centre.
As was established in chapters 1 and 2, the antecedent of the anaphor han (he) in (4-15b) cannot
be resolved differently from the anaphor in (4-15a) without consulting some sort of knowledge
source. However, the information present in the EPAS list can be used as domain knowledge in
the process of resolving these anaphors. For the anaphor in (4-15a) other occurrences of the
predicate etterlyse (call-for) can be consulted. This would produce the following list:
(4- 16)
etterlyse,?,bilfører
etterlyse,politi,bilfører
etterlyse,politi,person
etterlyse,politi,syklist
call-for,?,driver
call-for,police,driver
call-for,police,person
call-for,police,biker
It is clear from the list that etterlyse tends to co-occur with politi as its first argument, and that it
does not occur with any other first arguments. Through the concept association we know that
politi and lensmann (sergeant) both belong to the same concept group. Consequenntly, we also
know that politi and lensmann both occur in the similar environments and thus share features.
Given that the possible antecedents in (4-15a) are lensmann and gjerningsmann (perpetrator),
the consultation of the EPAS list and the concept groups leads us to select lensmann as the
80

antecedent for (4-15a). In the case of (4-15b) the EPAS list is unfortunately not equally helpful.
Other occurrences of observere (observe) are:
(4- 17)
observere,?,23-åring
observere,?,bile
observere,?,person
observe,?,23-year-old
observe,?,car
observe,?,person
Neither the EPAS in (4-17), nor the concept groups from section 4.2.2, give us any clues as to
whether lensmann or gjerningsmann is a more likely second argument in connection with the
predicate observere. This can be explained by two circumstances; firstly, the example sentences
in (4-15) are constructed and therefore do not reflect examples from the data set, secondly, the
small size of the data material obviously limits the extent to which one can expect that all valid
patterns of the domain are in fact found in the data set.
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5 Final remarks
5.1 Is a parser vital for the extraction process?
An initial assumption during the development of the method in this thesis was that it was of high
importance to found the extraction method on a syntactic parse of the text collection. As the
reader will recall, the reasons for this assumption were elaborated in chapter 3 and will therefore
not be discussed further here. However, as a means of evaluating the extraction method, the
texts in the text collection were processed using the Oslo-Bergen Tagger (OBT 2005). This is a
part of speech (POS) tagger which among other options offers the user a syntactic
disambiguation of the input text. The texts were POS tagged using the web version of the tagger
and structures corresponding to subject-verb-object relations were manually extracted from the
output. This yielded a list of 169 structures, 26 of them with pronouns. The structures were
extracted using a quite rudimentary method; for example no differences were made between
active and passive versions of the same predicate. This resulted in a list featuring exactly the
problematic issues discussed in chapter 3; the arguments were represented (and subsequently
structured) not according to thematic roles, but merely according to their syntactic roles in the
sentence. As a result, the list did not reflect characteristic arguments of the different predicates
to the same degree as the EPAS list did. The list of the POS-based structures is available in
Appendix F.
Consider the FCA diagram in Figure 7 below. Figure 7 shows part of the FCA diagram created
for the POS-based structures; the section of the diagram with the argument politi (police) as
starting point is highlighted. When comparing this figure to the corresponding figure for the
EPAS list (Figure 5 in section 3.6.4), it is quite clear that the POS-based list is significantly less
generalisable. There are no clear groupings of arguments which display specific behaviour
through their combination with a certain subset of predicates. Because formal subjects of both
active and passive sentences are realised as first arguments in this extraction, it is hardly
possible to group arguments into groups of semantically related words based on their
distribution. As can be seen from the diagram, politi co-occurs with both sykepleiestudent
(student nurse) and bilfører (driver), as well as other, more relevant, arguments.
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Figure 7
Interestingly enough, however, the POS-based list of structures proved to be just as well suited
as the EPAS list for subsequent classification using TiMBL. When training and testing the
classifier on the POS-based structures, it assigned the correct antecedent in 57,69% (15/26) of
the test cases. In comparison, the EPAS classifier performed with an accuracy of 57,69% when
trained and tested on argument 1, and with an overall accuracy of 46,87%.
These results are interesting mainly because they show that for the purposes of using a memory
based classifier, an extraction method based on a syntactic parser does not necessarily provide
better results than a POS-tagger based method. Even though the list of extracted structures was
decidedly poorer than the EPAS list, especially because it contained “wrong” information in the
sense that logical objects were listed as subjects by virtue of their syntactic role, it provided
useful input for the classification process. It is, however, as suggested by the FCA diagram
above, likely that the POS-based list would be of less use for the concept association phase,
since this approach relies on the presence of similar entities in similar positions in the structures.
As a conclusion, it can probably be stated that the advantages of using a syntactic parser in the
83

extraction process are more unclear than first presumed. For the purposes of aiding in anaphora
resolution, it may well be that an extraction method can perform equally well when based on
more shallow/superficial processing methods.
5.2 Summary and conclusions
This thesis has described a method for corpus-based semantic categorisation of predicates and
arguments in a limited thematic domain. The aim of the project was to create a means of
automatically inferring selectional restrictions corresponding to real-world knowledge of the
domain of the text collection. The classification of the predicates and arguments extracted from
the text collection resulted in several concept groups, where each concept group displayed a
particular distribution in the text collection.
In the introduction of the thesis, it was stated that a chief goal of the project was to assess the
value of using co-occurrence patterns to create concept groups which can act as an aid in the
process of pronoun resolution. The concept groups were thought to function as an intuition
about which word to expect in a given environment. Two criteria were formulated with regards
to the evaluation of the results obtained by the project:
• were the concept groups created valid for the domain of the text collection?
• were the concept groups useful in the process of anaphora resolution?
Through classification and testing of the extracted data set some remarks can be made with
regards to these two criteria. The concept groups that emerged as a product of the association
performed in the classification phase did indeed seem to constitute valid groupings of
semantically similar words. The concept groups were made based on the contextual distribution
of arguments in the text collection and represent groups of words which “keep the same
company” and tend to occur in similar environments. They are valid groupings for the domain of
the text collection and confirm the intuition that similar words display similar distribution, and
thus similar behaviour in the data set. The tests performed with the concept groups show that
they do contribute to heightening the success rate of the MBL classifier; when testing with
EPAS containing pronouns the classifier assigned the correct concept group as antecedent in
78% of the instances, in comparison to an almost 47% success rate without concept groups.
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When testing on knowledge-dependent anaphors and on anaphors which do not have an
explicitly mentioned antecedent in the text, it was evident that concept groups contribute with
interesting information. Ideally, a referring guessing helper using concept groups should be
consulted as part of an anaphora resolution system. In the event of several possible antecedent
candidates motivated from the text and proposed by the system, the concept groups in
connection with the context pattern of the anaphor can provide useful information about which
type of antecedent is likely. In this way the concept groups resemble information about the valid
contextual patterns for the domain.
The stumbling block of the method in this thesis is the limited dimension of it. The data set used
for the analyses is fairly small and as a consequence the results are less powerful than they could
have been. The extraction method is at best semi-automatic and employs far too much manual
intervention. This is a reoccurring problem for many methods within the field of NLP, Mitkov
for example says that “only a few anaphora resolution systems operate in fully automatic mode”
(Mitkov 2001, p. 111). Most systems rely on manual pre-editing of the input texts, some
methods are only manually simulated. In order for a method to be fully automatic there should
be no human intervention at any stage (Mitkov 2001, p. 114). In the case of the project described
in this thesis, the extraction method is far too manually manipulated to be considered automatic.
The scope of the results is naturally influenced by the limitations of the data set, but regardless
of the size of the data set and the manual intervention employed in the extraction phase, the
method shows promising results. It was clear from the beginning that this would be a pilot study
aiming at providing an indication of the usefulness of the method.
In view of the results, it should be stated that using contextual distribution to derive intuitions of
selectional restrictions in a limited domain is a promising venture. The results obtained in this
project suggest that the distribution of predicates and arguments within a closed domain has a
potential use as a representation of real-world knowledge. More definite conclusions about the
extent to which such a method captures enough relevant intuitions about real-world knowledge
to replace it in an anaphora resolution system can, however, first be made in the event of a
larger-scale study.
85

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Appendix A: Ekstraktor.pl – algorithm
The algorithm behind Ekstraktor is divided into two separate parts: information retrieval
from the Prolog file and processing of the information that was found and stored.
First a Prolog output file is opened and each line of the file is read. Based on patternmatching,
lines from the file are stored in different arrays according to which pattern they
match.
Subsequent to the information-extraction from the Prolog file, the information stored in
the arrays is processed for the purpose of creating predicate-argument structures. In the
following. I will give a brief outline of the processing steps. I will do this by describing
each of the central functions in Ekstraktor.
The term epmor (eng: ep mother) corresponds to the first EP in the ARG0ep-array, in
most cases meaning the EP “in question”.
finnHoved();
Finds the semantic forms of the main/first predicate-argument structure in the sentence.
This function calls the following (sub)functions:
finnEP1();
Since the entities parsed are full sentences, the main structures is limited to having a verb
as its head. This function searches the array catsuff for a pattern with the first member of
ARG0ep as its EP. If such a pattern is found, the EP is discarded and the first members of
arrays ARG0ep and ARG0verdi are removed.
finnPred();
Finds the semantic value of the sentence’s predicate/ARG0. Goes through the array
semform searching for a pattern with the first member of ARG0ep as EP. If such a pattern
is found, the semantic form is retrieved and stored in the array predikat.
In order to avoid an “empty” semantic form if the argument is a proper noun, it is
checked if the retrieved form matches named. If so, the array navn is searched for a
pattern with the first member of ARG0ep as EP. If such an entry is found, predikat is
emptied and the new semantic form is stored there.
Some predicates have an extra attribute which is stored in the array prt. Each line in this
array is searched for a pattern with the first member of ARG0ep as EP. If such an entry is
found, the semantic form is retrieved and stored in the array ekstra.
lagVerbStruktur();
Creates the correct verbal structure for the predicate. This is for the cases where the
predicate has an additional attribute – as in the predicate “lete etter” (Eng: look for). The
89

function checks if there are any members in the array ekstra. If so, the main predicate and
this extra attribute are stored in the array hovedpred.
If there is nothing stored in ekstra, the main predicate is simply stored in the array
hovedpred.
finnARG1();
Returns the semantic form of argument 1 and stores it in the array ARG1. First the arrays
ARGxep, ARGxverdi, ep and ARGx are emptied and subsequently set to the corresponding
argument 1 values. Then finnARGx() is called.
finnARGx();
Generalized function that finds the EP where the semantic form of the argument
in question is stored, calls finnARGxsemform() and returns the semantic form.
For ARG1 the actions are as follows:
Goes through each member in ARG1ep. If the first member of ARG0ep is EP, the
entry on the same index in ARG1verdi is stored as ARGx. Goes through each
member in ARG1verdi. If ARGx matches an entry in ARG1verdi, the entry on the
same index in ARG0ep is retrieved and stored in the array ep.
finnARGxsemform() is called.
finnARGxsemform();
Generalized function that finds the semantic form of the argument in
question.
For ARG1 the actions are as follows:
Find semantic form of double predicates, if there are any:
The variable epARGx is set to the first member of the array ep (this array
holds the indexes of EPs where the semantic form of ARGx is stored).
Goes through the array index (holds pointers to semantic forms of double
arguments), if an entry matches epARGx as EP, the index pointer is
retrieved and stored in the array ARGxind. Goes through semform, if an
entry matches epARGx as EP, it is removed from the array.
If there are any entries in ARGxind, each member is looked at. If an entry
matches an entry in ARG0verdi, the entry on the same index in ARG0ep is
added to the array liste. The array semform is gone through, if an entry
matches an entry from liste as EP, the semantic form is retrieved and
stored in the array ARGx.
Else find the semantic form of the argument in question:
Goes through semform , if an entry matches epARGx as EP the semantic
form is retrieved and stored in the array ARGx. If the element stored in
ARGx matches ‘named’, the proper noun must be found. The array navn is
searched for a pattern with epARGx as EP. The semantic form is retrieved
and stored in the array ARGx.
The contents of the array ARGx is stored in the array ARG1.
The contents of ARG1 is in finnHoved() stored as HovedARG1.
90

finnARG2();
This function has exactly the same performance as finnARG1(), only with
correspondingly different variable and array names.
The contents of ARG2 is in finnHoved() stored as HovedARG2.
fjernEP();
Removes elements from the arrays ARG1ep, ARG1verdi, ARG2ep and ARG2verdi if they
belong to the main EP.
Goes through ARG1ep and ARG2ep. If the first member of ARG0ep matches the entry,
the entry and the entry on the same index in the value-array is removed.
The first entry in ARG0ep and ARG0verdi is subsequently removed.
sjekkEkstra();
Checks if there are more predicate-argument structures to be found, calls finnResten() if
there are.
Goes through ARG1ep and ARG2ep trying to match each element in ARG0ep. If there is a
match, there exists a predicate with a belonging argument and finnResten() is called.
finnResten();
Finds the remaining predicate-argument structures.
Calls the following (sub)functions:
finnPred();
finnARG1();
finnARG2();
fjernEP();
sjekkEkstra();
lagStruktur();
Creates the predicate-argument structures as printed to the output file.
If HovedARG1 or HovedARG2 contains more than one element, each element is printed
with predicate and argument 2.
Else, hovedpred,HovedARG1 and HovedARG2 are printed to file, separated by commas.
91

Appendix B: Ekstraktor.pl – program code
Perl script Ekstraktor.pl
#åpner fil som angis fra kommandolinjen når programmet kjøres
open(FIL, $ARGV[0]) or die("Kan ikke åpne filen!!\n");
#leser hver linje i filen og legger linjen inn i forskjellige arrayer avhengig av hva som
leses. Får lagret all informasjon som er nødvendig for å trekke ut pred-argstrukturene
while ($linjeFraFil = ) {
#legger indexverdi i @ARG0ep og arg0-verdi i @ARG0verdi dersom linjen fra filen
inneholder ARG0
if ($linjeFraFil =~ m/ARG0/){
henteVerdi();
push(@ARG0ep, $ep);
push(@ARG0verdi, $verdi);
}
#legger indexverdi i @ARG1ep og arg1-verdi i @ARG1verdi dersom linjen fra filen
inneholder ARG1
if ($linjeFraFil =~ m/ARG1/){
henteVerdi();
push(@ARG1ep, $ep);
push(@ARG1verdi, $verdi);
}
#legger indexverdi i @ARG2ep og arg2-verdi i @ARG2verdi dersom linjen fra filen
inneholder ARG2
if ($linjeFraFil =~ m/ARG2/){
henteVerdi();
push(@ARG2ep, $ep);
push(@ARG2verdi, $verdi);
}
#legger indexverdi i @ARG3ep og arg3-verdi i @ARG3verdi dersom linjen fra filen
inneholder ARG3
if ($linjeFraFil =~ m/ARG3/){
henteVerdi();
push(@ARG3ep, $ep);
push(@ARG3verdi, $verdi);
}
#legger lest linje inn i @restriksjoner dersom den inneholder 'BODY'
if ($linjeFraFil =~ m/'BODY'/){
push(@restriksjoner, $linjeFraFil);
}
#legger lest linje inn i @restriksjoner dersom den inneholder 'RSTR'
if ($linjeFraFil =~ m/'RSTR'/){
push(@restriksjoner, $linjeFraFil);
}
#legger lest linje inn i @semform dersom den bl.a inneholder 'semform'
if ($linjeFraFil =~ m/'relation'\),semform\(/){
push(@semform, $linjeFraFil);
}
#legger lest linje inn i @cat dersom den inneholder '_CAT'
if ($linjeFraFil =~ m/'_CAT'\)/){
push(@cat, $linjeFraFil);
}
#legger lest linje inn i @catsuff dersom den inneholder '_CATSUFF'
if ($linjeFraFil =~ m/'_CATSUFF'\)/){
push(@catsuff, $linjeFraFil);
}
92

#legger lest linje inn i @prt dersom den inneholder '_PRT'
if ($linjeFraFil =~ m/'_PRT'\)/){
push(@prt, $linjeFraFil);
}
#legger lest linje inn i @index dersom den inneholder 'L-INDEX'
if ($linjeFraFil =~ m/'L-INDEX'\)/){
push(@index, $linjeFraFil);
}
#legger lest linje inn i @index dersom den inneholder 'R-INDEX'
if ($linjeFraFil =~ m/'R-INDEX'\)/){
push(@index, $linjeFraFil);
}
#legger lest linje inn i @navn dersom den inneholder 'CARG'
if ($linjeFraFil =~ m/'CARG'\)/){
push(@navn, $linjeFraFil);
}
} #slutt while-loop
close(FIL);
#Her begynner prosesseringen av info hentet ut fra inputfilen:
#fjerner ep'er som inneholder informasjon man vil se bort fra
fjernRestri();
#fjerner første ep dersom den ikke har kategori 'v'
finnCat();
#print("ARG0ep = \n@ARG0ep\nARG0verdi = \n@ARG0verdi\nARG1ep = \n@ARG1ep\nARG1verdi =
\n@ARG1verdi\nARG2ep = \n@ARG2ep\nARG2verdi = \n@ARG2verdi\n");
#finner hovedstrukturen
finnHoved();
#print("ARG0ep = \n@ARG0ep\nARG0verdi = \n@ARG0verdi\nARG1ep = \n@ARG1ep\nARG1verdi =
\n@ARG1verdi\nARG2ep = \n@ARG2ep\nARG2verdi = \n@ARG2verdi\n");
#print("@semform\n");
#print("@navn\n");
#finner predikat-argumentstruktur nr2
#sjekkEkstra();
#finnResten();
#legger til predikat-argumentstrukturene til slutt i filen som angis
open(OUTPUTFIL, ">>strukturer.txt") or die("kan ikke skrive til fil\n");
#open(OUTPUTFIL, ">>home/unni/Hovedoppgave/parse/pas-strukturer.txt") or die("kan ikke
skrive til fil\n");
sjekkEkstra();
#lager hovedstrukturen
lagStruktur();
close(OUTPUTFIL);
#her kommer alle subfunksjonene:
#henteVerdi():
#henter ut relasjonsindex og verdi til ARGx fra en linje fra
#inputfilen og lagrer dem i $ep og $verdi
93

#Linje fra fil deles opp ved komma og lagres i @utenKomma. Verdiene hentes ut med
substr().
sub henteVerdi {
@utenKomma = split(/,/, $linjeFraFil);
push(@args, @utenKomma);
}
$ep = substr(@utenKomma[1], 12, 2);
if ($ep =~ /\)/){
$ep = split(/\)/, $ep);
}
$verdi = substr(@utenKomma[3], 4, 2);
if ($verdi =~ /\)/){
$verdi = split(/\)/, $verdi);
}
#finnHoved:
#finner hoved pred-argstrukturen
#vanligvis predikat,arg1,arg2
sub finnHoved {
}
finnEP1();
finnPred();
lagVerbStruktur();
finnARG1();
@HovedARG1 = @ARG1;
finnARG2();
@HovedARG2 = @ARG2;
fjernEP();
sub sjekkEkstra {
foreach $element (@ARG0ep){
foreach $element2 (@ARG1ep){
if ($element =~ $element2){
#print("match!\n");
finnResten();
}
}
foreach $element3 (@ARG2ep){
if ($element =~ $element3){
#print("match2!\n");
finnResten();
}
}
}
#print("ARG0ep: @ARG0ep\nARG1ep: @ARG1ep\nARG2ep: @ARG2ep\n");
}
sub finnResten {
finnPred();
finnARG1();
finnARG2();
print(OUTPUTFIL "@predikat,@ARG1,@ARG2\n");
print("@predikat,@ARG1,@ARG2\n");
fjernEP();
splice (@predikat);
splice (@ARG1);
splice (@ARG2);
sjekkEkstra();
}
94

sub fjernEP{
#fjerner elementer fra @ARG1ep/verdi og @ARG2ep/verdi dersom de hører til hovedep'en
$epmor = $ARG0ep[0];
for ($i = 0; $i < @ARG1ep; $i++){
if ($epmor =~ $ARG1ep[$i]){
splice(@ARG1ep, $i, 1);
splice(@ARG1verdi, $i, 1);
#print("@semform\n");
}
}
for ($i = 0; $i < @ARG2ep; $i++){
if ($epmor =~ $ARG2ep[$i]){
splice(@ARG2ep, $i, 1);
splice(@ARG2verdi, $i, 1);
}
}
shift(@ARG0ep);
shift(@ARG0verdi);
}
sub finnCat {
foreach $linje (@cat){
$epmor = $ARG0ep[0];
if ($linje =~ m/\(attr\(var\($epmor\)/){
@utenDings = split(/\'/, $linje);
push (@args, @utenDings);
$epcat = substr(@utenDings[3],0,1);
#print("$epcat\n");
if ($epcat !~ /v/){
shift(@ARG0ep);
shift(@ARG0verdi);
}
}
}
}
#finnEP1():
#finner den ep'en som skal være utgangspunkt for predikat-argumentstrukturen
#$epmor settes til første element i ARG0-arrayen
#gå gjennom @catsuff, hvis linjen som leses matcher $epmor fjernes første element i
@ARG0ep og @ARG0verdi
sub finnEP1 {
foreach $linje (@catsuff){
$epmor = $ARG0ep[0];
if ($linje =~ m/\(attr\(var\($epmor\)/){
shift(@ARG0ep);
shift(@ARG0verdi);
}
}
}
#finnPred():
#finner den semantiske verdien til ARG0/predikatet i setningen
#$epmor settes til første element i @ARG0ep
#hvis lest linje bl.a matcher $epmor og 'semform', splittes den ved ' og elementene
legges i @utenDings
#@verb settes til fjerde element i @utenDings
#dersom linjen matcher bl.a $epmor og '_PRT', splittes linjen ved ' og den semantiske
formen legges til @verb2
sub finnPred {
$epmor = $ARG0ep[0];
foreach $linje (@semform){
if ($linje =~ /\(attr\(var\($epmor\),'relation'\),semform/){
95

}
}
}
@utenDings = split(/\'/, $linje);
push(@args, @utenDings);
@pred = @utenDings[3];
push(@predikat, @pred);
if ($predikat[0] =~ /named/){
foreach $verdi (@navn){
if ($verdi =~ /\(attr\(var\($epmor\)/){
splice(@pred);
splice(@predikat);
@uten = split(/\'/, $verdi);
push(@arg, @uten);
@pred = @uten[3];
push(@predikat, @pred);
#print("@predikat\n");
}
}
}
foreach $linje (@prt){
if ($linje =~ /\(attr\(var\($epmor\)/){
@utenDings = split(/\'/, $linje);
push(@args, @utenDings);
@ekstr = @utenDings[3];
push(@ekstra, @ekstr);
}
}
sub finnARG1 {
$imax = 0;
splice(@ARGxep);
splice(@ARGxverdi);
splice(@ep);
splice(@ARGx);
}
$imax = @ARG1ep;
@ARGxep = @ARG1ep;
@ARGxverdi = @ARG1verdi;
finnARGx();
@ARG1 = @ARGx;
sub finnARG2 {
$imax = 0;
splice(@ARGxep);
splice(@ARGxverdi);
splice(@ep);
splice(@ARGx);
}
$imax = @ARG2ep;
@ARGxep = @ARG2ep;
@ARGxverdi = @ARG2verdi;
finnARGx();
@ARG2 = @ARGx;
sub finnARG3 {
$imax = 0;
splice(@ARGxep);
splice(@ARGxverdi);
splice(@ep);
96

}
splice(@ARGx);
$imax = @ARG3ep;
@ARGxep = @ARG3ep;
@ARGxverdi = @ARG3verdi;
finnARGx();
@ARG3 = @ARGx;
sub finnARGx {
$epmor = $ARG0ep[0];
}
for ($i = 0; $i < $imax; $i++){
if ($epmor =~ /$ARGxep[$i]/){
$ARGx = $ARGxverdi[$i];
$imax2 = @ARG0verdi;
for ($ii = 0; $ii < $imax2; $ii++){
if ($ARGx =~ /$ARG0verdi[$ii]/){
push(@ep, $ARG0ep[$ii]);
}
}#slutt for2
}
}#slutt for1
finnARGxsemform();
#fjernRestri():
#setter @ARGxep og @ARGxverdi til ARG0-verdiene
#kjører restrik()
sub fjernRestri {
@ARGxep = @ARG0ep;
@ARGxverdi = @ARG0verdi;
restrik();
@ARG0ep = @ARGxep;
@ARG0verdi = @ARGxverdi;
}
#restrik():
#Går gjennom @restriksjoner og @index og fjerner verdier fra @ARG0ep og @ARG0verdi dersom
#disse arrayene inneholder informasjon om dem
sub restrik {
$imax = @ARGxep;
}
for ($i = 0; $i < $imax; $i++){
foreach $linje (@restriksjoner){
if ($linje =~ /\(attr\(var\($ARGxep[$i]\)/){
splice(@ARGxep, $i, 1);
splice(@ARGxverdi, $i, 1);
}
}
}
foreach $linje (@index){
@utenKomma = split(/,/, $linje);
push(@args, @utenKomma);
$ep = substr(@utenKomma[1], 12, 2);
push(@indexep, $ep);
#print("@indexep\n");
#print("@semform\n");
foreach $linje (@indexep){
97

}
}
for ($i = 0; $i < @semform; $i++){
if ($semform[$i] =~ /\(attr\(var\($linje\)/){
splice(@semform, $i, 1);
#print("@semform\n");
}
}
#restrimatch():
#fjerner ep'er som ikke inneholder informasjon om den semantiske formen
#går gjennom hvert element i @restriksjoner og for hvert element settes $epARG1 til
første element i @ep2
#hvis elementet fra @restriksjoner inneholder $epARG1 som indexverdi, fjernes det fra
@ep2
sub restrimatch {
foreach $linje (@restriksjoner){
$epARGx = $ep[0];
if ($linje =~ m/\(attr\(var\($epARGx\)/){
shift(@ep);
}
}
}
#restrimatch for doble argument2:
#samme fremgangsmåte som for restrimatch(), men med andre variabler etc
sub restrimatch4 {
$imax = @liste;
for ($i = 0; $i < $imax; $i++){
foreach $linje (@restriksjoner){
$epARGx = $liste[$i];
if ($linje =~ m/\(attr\(var\($epARGx\)/){
splice(@liste,$i,1);
}
}
}
}
#MODULARISERT VERSJON - GENERISK FUNKSJON FOR Å FINNE SEMANTISK FORM
#finnARGxsemform():
sub finnARGxsemform {
$epARGx = $ep[0];
foreach $linje (@index){
if ($linje =~ /\(attr\(var\($epARGx\)/){
@utenKomma = split(/,/, $linje);
push(args, @utenKomma);
$verdi = substr(@utenKomma[3],4,2);
push(@ARGxind, $verdi);
for ($i = 0; $i < @semform; $i++){
if (@semform[i] =~ /\(attr\(var\($epARGx\)/){
splice(@semform, $i, 1);
}
}
}
}
#finner ep hvor element i @ARGxind er verdien til ARG0 og legger dem i array @liste
if (@ARGxind != 0){
foreach $element (@ARGxind){
$imax = @ARG0verdi;
for ($i = 0; $i < $imax; $i++){
if($element =~ /$ARG0verdi[$i]/){
98

}
else{
}
}
}
push(@liste, $ARG0ep[$i]);
foreach $linje (@semform){
foreach $element (@liste){
if ($linje =~ /\(attr\(var\($element\)/){
@utenDings = split(/\'/, $linje);
push(args, @utenDings);
$ARG = @utenDings[3];
push(@ARGx, $ARG);
}
}
}
foreach $linje (@semform){
if ($linje =~ /\(attr\(var\($epARGx\)/){
@utenDings = split(/\'/, $linje);
push(args, @utenDings);
@ARGx = @utenDings[3];
}
}
if ($ARGx[0] =~ /named/){
foreach $verdi (@navn){
if ($verdi =~ /\(attr\(var\($epARGx\)/){
splice(@ARGx);
#splice(@predikat);
@uten = split(/\'/, $verdi);
push(@arg, @uten);
@ARGx = @uten[3];
#push(@predikat, @pred);
#print("@predikat\n");
}
}
}
}
}
#slutt finnARGxsemform
#lagStruktur():
#lager predikat-argumentstrukturen som skal skrives til outputfilen
#hvis det finnes et element i @verb2 blir det lagt til @verb1
#@verb, $ARG1, $ARG2 og $ARG3 skrives til outputfilen
sub lagStruktur {
#lagVerbStruktur();
#lager riktig arg1 struktur
if (@HovedARG1 > 1){
foreach $element (@HovedARG1){
print(OUTPUTFIL "\n@hovedpred,$element,@HovedARG2\n");
print("@hovedpred,$element,@HovedARG2\n");
}
}
# print(OUTPUTFIL "\n@hovedpred,@ARG1sem[0],@ARG2,$ARG3\n");
if (@HovedARG2 > 1){
foreach $element (@HovedARG2){
print(OUTPUTFIL "\n@hovedpred,@HovedARG1,$element\n");
99

}
}
print("@hovedpred,@HovedARG1,$element\n");
else {
print(OUTPUTFIL "\n@hovedpred,@HovedARG1,@HovedARG2\n");
print("@hovedpred,@HovedARG1,@HovedARG2\n");
}
#if (@predikat != 0){
# print(OUTPUTFIL "$predikat[0],@ARG1,@ARG2\n");
# print("$predikat[0],@ARG1,@ARG2\n");
#}
}
#lager riktig verb-struktur, feks "lete etter"
sub lagVerbStruktur {
if(@ekstra != 0){
@hovedpred = ($predikat[0],$ekstra[0]);
shift(@ekstra);
shift(@predikat);
}
}
else {
@hovedpred = $predikat[0];
shift(@predikat);
}
100

101
Appendix C: the EPAS list
23-år-gammel,student,
aktuell,tidsrom,
analysere,Kripos-spesialist,spor
ankomme,etterforsker,
ankomme,etterforsker,
ankomme,etterforsker,åsted
antyde,politi,
avhøre,,person
avhøre,,vedkommende
avhøre,politi,vitne
avklare,obduksjon,
bede om,lensmann,assistanse
bede om,politi,bistand
bede,lensmann,
bede,lensmann,
bede-om,Fonn,bistand
bekrefte,lensmann,
bekrefte,politi,
bekrefte,politi,
bekrefte,politimester,
bistå,etterforsker,lensmann
bistå,etterforsker,politi
bistå,etterforsker,politi
bli,Anne,offer
bo,23-åring,studentkollektiv
bo,Anne,studentkollektiv
bo,beboer,studentkollektiv
bo,Slåtten,studentkollektiv
brutal,drapsmann,
desperat,rop,
død,sykepleiestudent,
drepe,,kvinne
drepe,,pron
drepe,,pron
drepe,,Slåtten
drepe,gjerningsmann,kvinne
drept,sykepleiestudent
ekstra,patrulje,
endelig,rapport,
etterforske,medarbeider,drap
etterlyse,,bilfører
etterlyse,,bilfører
etterlyse,politi,bilfører
etterlyse,politi,person
etterlyse,politi,syklist
etterlyse,politi,syklist
etterlyst,syklist,
etterlyst,syklist,
fastslå,politi,
finkjemme,politi,bygning
finne,,død
finne,,død
finne,,død
finne,,kvinne
finne,,lommebok
finne,,pron
finne,,pron
finne,,sykepleiestudent
finne,forbipasserende,sykepleiestudent
finne,leteaksjon,kvinne
finne,politi,drapsmann
forfølge,,pron
forkaste,politi,teori
fortelle,beboer,politi
fortelle,Fonn,
fortelle,Fonn,

102
fra-kripos,etterforsker,
fra-kripos,etterforsker,
fra-kripos,etterforsker,
første,praksisdag,
førsteårs,sykepleiestudent,
førsteårs,sykepleiestudent,
få,politi,svar
få,politi,tips
få,pron,rapport
få,pron,telefon
gi,Fonn,opplysning
gi,kamera,indikasjon
gi,lensmann,opplysning
gi,politi,informasjon
gi,politi,opplysning
gi,vitneavhør,indikasjon
gjemme,drapsmann,
gjemme,gjerningsmann,
gjemme,gjerningsmann,
gjennomføre,,rekonstruksjon
gjennomgå,tekniker,studentkollektiv
gjennomsøke,politi,studenthybel
gjøre,politi,avhør
gjøre,politi,rundspørring
gå-gjennom,polititjenestefolk,material
ha,etterforsker,observasjon
ha,politi,medarbeider
ha,politi,teori
ha,pron,observasjon
ha,pron,teori
ha,pron,teori
holde åpen,politi,mulighet
holde,politi,kort
holde,politi,pressekonferanse
holde-åpen,politi,mulighet
høre,pron,rop
høre,pron,rop
høre,vitne,rop
høy,rop,
håpe,politi,
identifisere,politi,pron
igangsette,,leteaksjon
informere,,politi
jobbe-utfra,pron,teori
kartlegge,pron,bevegelse
kartlegge,pron,bevegelse
kjenne,generic-nom,Slåtten
kjenne,politi,dødsårsak
komme-i-kontakt-med,politi,bilfører
komme-i-kontakt-med,politi,generic-nom
komme-i-kontakt-med,pron,bilfører
komme-i-kontakt-med,pron,syklist
komme-inn,tips,
kommentere,pron,
kontakte,etterforsker,vitne
kriminell,handling,
kriminell,handling,
kriminell,handling,
melde-savnet,,student
melde-seg,syklist,
melde-seg,syklist,politi
melde-seg,syklist,politi
mene,etterforsker,
mene,politi,
merke,kjæreste,
mistenkelig,dødsfall,
mulig,teori,
muntlig,rapport
møte-opp-til,pron,praksisdag
ny,tips,
nær,opplysning

103
obdusere,,kvinne
observere,,23-åring
observere,,bile
observere,,person
opplyse,Fonn,
opplyse,vitne,
oppmerksom,kvinne,
overfalle,,Slåtten
plombere,politi,hybelhus
pågå,leteaksjon,
påkledd,Slåtten,
rigge,etterforsker,lyskaster
samle,politi,observasjon
sanke-inn,politi,video
savne,,kvinne
se,pron,Slåtten
se,vitne,kvinne
sentral,vitne,
sette-igang,,leteaksjon
sette-inn,politi,patrulje
si,Fonn,
si,Fonn,
skade,,kvinne
skje-med,generic-nom,kvinne
slutte-seg-til,pron,Førde-politi
sperre av,,hybelhus
sperre av,politi,åsted
spesiell,teori
spesiell,teori,
stenge av,politi,studentkollektiv
stor,leteaksjon,
systematisere,,tips
søke-med,politi,hund
ta,pron,utgangspunkt
ta-høyde-for,lensmann,eventualitet
ta-kontakt-med,politi,vitne
ta-kontakt-med,syklist,politi
taktisk,etterforsker,
teknisk,etterforsker,
teknisk,etterforsker,
teknisk,spor,
teknisk,spor,
tidlig,teori,
tilfeldig,forbipasserende,
tilfeldig,offer,
trenge,,vitne
tro,lensmann,
tro,politi,
tro,pron,
ukjent,gjerningsmann,
ukjent,person,
undersøke,,minibankaktiviteter
undersøke,,mobiltelefontrafikk
undersøke,,område
undersøke,,overvåkningsfilmer
undersøke,etterforsker,åsted
undersøke,politi,aktivitet
understreke,Fonn,
understreke,pron,
understreke,pron,
understreke,pron,
understreke,pron,generic-nom
varsle,pron,Kripos
velge,drapsmann,sykepleiestudent
velge,drapsmann,sykepleiestudent
ville,pron,kartlegge
vise,funn,
vise,funn,
vise,undersøkelse,
vite,politi,
være,Anne,offer

være,bilfører,vitne
være,bilfører,vitne
være,etterforskning,bred
være,kvinne,død
være,kvinne,død
være,kvinne,skadet
være,kvinne,Slåtten
være,lommebok,funn
være,pron,funn
være,pron,omkommet
være,rapport,klar
være,Slåtten,sykepleiestudent
være,syklist,vitne
være,vitne,kvinne
ønske,politi,
ønske,politi,
åpen,mulighet,
åpen,mulighet,
104

Appendix D: Text aligned with EPAS
SENTENCE EPAS METHOD
Kvinne funnet død i Førde. finne,,død
automatic
være,kvinne,død
automatic
Den savnede kvinnen i Førde er finne,,død
automatic
nå funnet død.
savne,,kvinne
automatic
være,kvinne,død
automatic
Politiet har gitt media
opplysninger om funnet.
gi,politi,opplysning automatic
Lensmannen bekrefter at kvinnen finne,,død
automatic
er funnet død.
bekrefte,lensmann,
automatic
Politiet har bedt Kripos om
bistand i søket etter kvinnen.
bede om,politi,bistand
automatic
23-åringen var førsteårs
sykepleiestudent i Førde.
førsteårs,sykepleiestudent,
edited
Hun møtte ikke opp til sin første,praksisdag,
manual
første praksisdag ved Førde
aldershjem.
møte_opp_til,pron,praksisdag
edited
Politiet ble informert. informere,,politi automatic
En leteaksjon ble satt igang. sette_igang,,leteaksjon edited
Leteaksjonen pågikk til kvinnen pågå,leteaksjon,
automatic
ble funnet.
finne,,kvinne
manual
finne,leteaksjon,kvinne
manual
Politiet holder alle muligheter holde åpen,politi,mulighet
edited
åpne i saken.
åpen,mulighet,
automatic
Etterforskerne vil ankomme i
morgen.
ankomme,etterforsker,
automatic
Et vitne hørte desperate rop om
hjelp.
Lensmannen har bedt om
assistanse fra Kripos.
Etterforskere fra Kripos skal
bistå lensmannen i
etterforskningen.
Etterforskerne forventes å
ankomme i løpet av dagen.
Den 23 år gamle studenten ble
meldt savnet tidlig søndag
morgen.
Anne Slåtten bodde i et
studentkollektiv i Førde.
Slåtten var førsteårs
sykepleiestudent i Førde.
Hun ble funnet omkommet i et
skogholt.
Et vitne opplyste at hun hadde
hørt høye rop.
Mandag holdt politiet en
pressekonferanse.
Lensmannen vil ikke gi nærmere
opplysninger om åstedet.
Beboerne i studentkollektivet
har fortalt politiet at de så
Slåtten lørdag kveld.
Politiet har sperret av
åstedet.
desperat,rop,
høre,vitne,rop
bede om,lensmann,assistanse
bistå,etterforsker,lensmann
fra_kripos,etterforsker,
ankomme,etterforsker,
23-år_gammel,student,
melde_savnet,,student
bo,Anne,studentkollektiv
bo,Slåtten,studentkollektiv
førsteårs,sykepleiestudent,
være,Slåtten,sykepleiestudent
finne,,pron
være,pron,omkommet
være,pron,funn
høy,rop,
opplyse,vitne,
høre,pron,rop
holde,politi,pressekonferanse
gi,lensmann,opplysning
nær,opplysning
fortelle,beboer,politi
se,pron,Slåtten
bo,beboer,studentkollektiv
sperre av,politi,åsted
105
automatic
automatic
automatic
automatic
edited
manual
edited
edited
edited
manual
edited
manual
automatic
manual
automatic
automatic
automatic
automatic
automatic
automatic
automatic
edited
automatic
manual
edited
Flere personer er avhørt i
saken.
avhøre,,person
automatic
Politiet holder alle muligheter holde_åpen,politi,mulighet
edited
åpne.
åpen,mulighet,
automatic
Kvinnen blir trolig obdusert i obdusere,,kvinne automatic

løpet av tirsdag.
Politiet håper obduksjonen vil
avklare hva som skjedde med
kvinnen
Mandag kveld ankom
etterforskere fra Kripos
åstedet.
Sent mandag kveld rigget
etterforskerne opp lyskastere.
Fonn vil ikke gi flere
opplysninger om åstedet.
Han vil ikke kommentere om
kvinnen var skadet.
håpe,politi,
avklare,obduksjon,
skje_med,generic-nom,kvinne
ankomme,etterforsker,åsted
fra_kripos,etterforsker,
rigge,etterforsker,lyskaster
gi,Fonn,opplysning
kommentere,pron,
skade,,kvinne
være,kvinne,skadet
holde,politi,kort
Politiet holder kortene svært
tett til brystet.
Det er ikke kommet inn mange komme_inn,tips,
tips i saken.
Tipsene skal nå systematiseres. systematisere,,tips
Fonn forteller at politiet vil
ta kontakt med vitner.
Politiet har flere mulige
teorier.
Det mest sentrale vitnet i
saken er en kvinne.
Hun skal ha hørt rop fra en
kvinne.
Politiet har stengt av
studentkollektivet der 23åringen
bodde.
Studentkollektivet vil bli
gjennomgått av teknikere.
Fonn har bedt om teknisk
bistand.
fortelle,Fonn,
ta_kontakt_med,politi,vitne
mulig,teori,
ha,politi,teori
sentral,vitne,
være,vitne,kvinne
høre,pron,rop
stenge av,politi,studentkollektiv
bo,23-åring,studentkollektiv
gjennomgå,tekniker,studentkollektiv
bede-om,Fonn,bistand
106
manual
manual
edited
edited
edited
automatic
automatic
automatic
automatic
manual
automatic
manual
automatic
manual
manual
automatic
automatic
automatic
automatic
automatic
automatic
edited
automatic
automatic
Politiet bekrefter at Slåtten bekrefte,politi,
automatic
ble drept.
drepe,,Slåtten
automatic
Undersøkelsene på stedet viser vise,undersøkelse,
automatic
at hun ble drept.
drepe,,pron
automatic
Politiet tror at Slåtten ble tro,politi,
automatic
overfalt.
overfalle,,Slåtten
automatic
De tror at kvinnen ble drept av tro,pron,
automatic
en ukjent gjerningsmann.
ukjent,gjerningsmann,
automatic
drepe,gjerningsmann,kvinne
automatic
Politiet fastslår at kvinnens fastslå,politi,
automatic
lommebok ikke er funnet.
finne,,lommebok
manual
være,lommebok,funn
automatic
Fonn opplyser at området ikke opplyse,Fonn,
automatic
er undersøkt.
undersøke,,område
automatic
Politiet har forkastet en
forkaste,politi,teori
automatic
tidligere teori.
tidlig,teori,
automatic
Politiet får senere i dag svar
på dødsårsaken.
få,politi,svar
automatic
Politiet har ikke gjennomsøkt
Slåttens studenthybel.
gjennomsøke,politi,studenthybel automatic
Hele hybelhuset ble sperret av. sperre av,,hybelhus edited
Politiet har plombert
hybelhuset.
plombere,politi,hybelhus automatic
Politiet skal finkjemme
finkjemme,politi,bygning
automatic
bygningen for tekniske spor. teknisk,spor,
automatic
De tekniske etterforskerne har teknisk,etterforsker,
automatic
undersøkt åstedet.
undersøke,etterforsker,åsted
automatic
To tekniske etterforskere
teknisk,etterforsker,
automatic
bistår politiet i Førde.
bistå,etterforsker,politi
automatic
En taktisk etterforsker fra bistå,etterforsker,politi
automatic
Kripos bistår politiet.
taktisk,etterforsker,
automatic
fra_kripos,etterforsker,
edited
Lensmannen tar høyde for alle ta_høyde_for,lensmann,eventualitet manual

eventualiteter.
Vi varslet Kripos. varsle,pron,Kripos automatic
Den døde sykepleierstudenten død,sykepleiestudent,
automatic
ble funnet av en tilfeldig finne,forbipasserende,sykepleiestudent
automatic
forbipasserende.
tilfeldig,forbipasserende,
edited
23-åringen ble sist observert
lørdag kveld.
observere,,23-åring automatic
Politiet vet at hun fikk en vite,politi,
automatic
telefon fra kjæresten sin. få,pron,telefon
automatic
Kjæresten merket ikke at noe
var galt.
merke,kjæreste, automatic
Vedkommende er avhørt. avhøre,,vedkommende automatic
En større leteaksjon ble
igangsette,,leteaksjon
automatic
igangsatt.
stor,leteaksjon,
edited
Politiet etterlyser en syklist. etterlyse,politi,syklist automatic
Den etterlyste syklisten har etterlyst,syklist,
manual
tatt kontakt med politiet. ta_kontakt_med,syklist,politi
manual
Fortsatt etterlyses to
bilførere.
etterlyse,,bilfører automatic
Politiet etterlyste i dag to
bilførere.
etterlyse,politi,bilfører automatic
To biler er observert på veien. observere,,bile automatic
Politiet ønsker å komme i
ønske,politi,
automatic
kontakt med bilførerne.
komme_i_kontakt_med,politi,bilfører
manual
Fonn understreker at bilførerne understreke,Fonn,
automatic
er vitner.
være,bilfører,vitne
edited
Fonn sier at han understreker understreke,pron,generic-nom
edited
dette.
si,Fonn,
automatic
Slåtten var påkledd da hun ble påkledd,Slåtten,
automatic
funnet drept.
finne,,pron
automatic
drepe,,pron
automatic
Vi vil nå kartlegge alle
kartlegge,pron,bevegelse
automatic
bevegelser på åstedet.
ville,pron,kartlegge
manual
Vi har ingen spesiell teori som ha,pron,teori
automatic
vi tar utgangspunkt i.
spesiell,teori,
automatic
ta,pron,utgangspunkt
automatic
Funnene på åstedet viser at det vise,funn,
automatic
er en kriminell handling.
kriminell,handling,
automatic
Det er ikke et mistenkelig mistenkelig,dødsfall,
automatic
dødsfall, men en kriminell
handling.
kriminell,handling,
automatic
Trenger flere vitner. trenge,,vitne automatic
Politiet ønsker å komme i
komme_i_kontakt_med,politi,generic-nom
edited
kontakt med alle som kjente ønske,politi,
automatic
Slåtten.
kjenne,generic-nom,Slåtten
automatic
Etterforskerne fra Kripos vil
kontakte vitner.
kontakte,etterforsker,vitne automatic
Politiet kjenner dødsårsaken. kjenne,politi,dødsårsak manual
Politimesteren bekrefter at de
har fått en muntlig rapport.
Han understreker at politiet
ikke vil gi informasjon om
dødsårsaken.
Politiet har ikke bekreftet
hvor kvinnen ble drept.
Politiet har nå 32 medarbeidere
som etterforsker drapet.
bekrefte,politimester,
få,pron,rapport
muntlig,rapport
understreke,pron,
gi,politi,informasjon
bekrefte,politi,
drepe,,kvinne
ha,politi,medarbeider
etterforske,medarbeider,drap
107
manual
manual
manual
manual
manual
manual
manual
manual
Syklisten meldte seg. melde_seg,syklist, manual
Den etterlyste syklisten har nå melde_seg,syklist,politi
manual
meldt seg til politiet i Førde. etterlyst,syklist,
manual
Fortsatt etterlyses to
bilførere.
etterlyse,,bilfører manual
Politiet etterlyste i dag
tidlig en syklist.
etterlyse,politi,syklist manual
I formiddag meldte syklisten melde_seg,syklist,politi manual

seg til politiet.
Jeg vil understreke at vi
ønsker å komme i kontakt med
både syklisten og bilførerne
som vitner, sier Fonn.
Vi vil nå kartlegge alle
bevegelser på funnstedet og i
boligen.
Vi har ingen spesiell teori som
vi jobber utifra nå.
Men funnene på åstedet viser at
det er en kriminell handling,
forteller Fonn.
I tillegg vil politiet gjøre en
rundspørring rundt åstedet i
løpet av dagen.
Den endelige rapporten vil være
klar på torsdag.
To Kripos-spesialister skal
analysere alle tekniske spor i
Førde.
De to sluttet seg til Førde-
politiet i går.
Alt av mobiltelefontrafikk,
overvåkingsfilmer og
minibankaktiviteter rundt
drapstidspunktet skal
undersøkes.
Slik kan politiet undersøke
aktiviteten i området
sykepleiestudenten ble funnet
drept.
Lensmann Kjell Fonn ber alle
som var i sentrum om å melde
seg.
Han understreker at
etterforskningen er svært bred.
Politiet har sanket inn videoer
fra alle overvåkningskameraer i
Førde.
Polititjenestefolk går gjennom
materialet.
Kameraene vil gi en indikasjon
på aktiviteten i Førde i det
aktuelle tidsrommet.
Gjerningsmannen gjemte seg i
busker på åstedet.
Det er sannsynlig at
gjerningsmannen gjemte seg i
busker ved åstedet.
Tror Anne ble et tilfeldig
offer.
understreke,pron,
komme_i_kontakt_med,pron,bilfører
komme_i_kontakt_med,pron,syklist
si,Fonn,
være,bilfører,vitne
være,syklist,vitne
108
manual
manual
manual
manual
manual
manual
kartlegge,pron,bevegelse manual
ha,pron,teori
spesiell,teori
jobbe_utfra,pron,teori
vise,funn,
kriminell,handling,
fortelle,Fonn,
manual
manual
manual
manual
manual
manual
gjøre,politi,rundspørring manual
endelig,rapport,
være,rapport,klar
analysere,Kripos-spesialist,spor
teknisk,spor,
manual
manual
manual
manual
slutte_seg_til,pron,Førde-politi manual
undersøke,,minibankaktiviteter
undersøke,,mobiltelefontrafikk
undersøke,,overvåkningsfilmer
undersøke,politi,aktivitet
finne,,sykepleiestudent
drept,sykepleiestudent
manual
manual
manual
manual
manual
manual
bede,lensmann, automatic
understreke,pron,
manual
være,etterforskning,bred
manual
sanke_inn,politi,video manual
gå_gjennom,polititjenestefolk,material manual
gi,kamera,indikasjon
aktuell,tidsrom,
gjemme,gjerningsmann,
gjemme,gjerningsmann,
tilfeldig,offer,
bli,Anne,offer
være,Anne,offer
manual
manual
automatic
manual
manual
Politiet avhører flere vitner. avhøre,politi,vitne automatic
Politiet har søkt med hunder på
åstedet.
søke_med,politi,hund edited
Politiet har samlet mange
observasjoner.
samle,politi,observasjon automatic
Politiet antyder at drapsmannen antyde,politi,
automatic
har valgt sykepleiestudenten
tilfeldig.
velge,drapsmann,sykepleiestudent
automatic
Vitneavhør gir indikasjoner på gi,vitneavhør,indikasjon
manual
at den brutale drapsmannen har brutal,drapsmann,
manual
valgt sykepleierstudenten
tilfeldig.
velge,drapsmann,sykepleiestudent
manual

Etterforskerne har flere
observasjoner.
Vitner så en kvinne som gikk
alene.
Politiet mener kvinnen er Anne
Slåtten.
Etterforskerne mener at hun
ikke ble forfulgt.
Drapsmannen kan ha gjemt seg i
busker ved åstedet.
Lensmannen ber unge kvinner
være oppmerksomme.
Politiet setter ikke inn ekstra
patruljer i Førde.
Politiet har fått flere nye
tips.
En rekonstruksjon ble
gjennomført på tirsdag.
Lensmannen tror at politiet
finner drapsmannen.
Politiet etterlyser fem
personer.
109
ha,etterforsker,observasjon manual
se,vitne,kvinne manual
mene,politi,
automatic
være,kvinne,Slåtten
automatic
mene,etterforsker,
automatic
forfølge,,pron
automatic
gjemme,drapsmann, manual
oppmerksom,kvinne,
bede,lensmann,
sette_inn,politi,patrulje
ekstra,patrulje,
automatic
automatic
edited
automatic
få,politi,tips
automatic
ny,tips,
automatic
gjennomføre,,rekonstruksjon automatic
tro,lensmann,
finne,politi,drapsmann
etterlyse,politi,person
automatic
atomatic
automatic
Personene er observert i Førde. observere,,person automatic
Politiet har ikke identifisert
dem.
identifisere,politi,pron
automatic
Politiet har gjort 1275 avhør. gjøre,politi,avhør automatic
De har fem observasjoner av ha,pron,observasjon
automatic
ukjente personer.
ukjent,person,
automatic

Appendix E: classify.pl – program code
#!/bin/perl
### Konfigurering og initialisering ###
# Predikat/argument-fil.
my $infile = "pred_argliste.txt";
# Initialiserer tomme datastrukturer.
my %pred_args1 = ();
my %pred_args2 = ();
my %arg1_preds = ();
my %arg2_preds = ();
# Leser inndata.
open(FILE, $infile);
my @lines = ;
close(FILE);
### Her bygges datastrukturen ###
foreach $line (@lines) {
# Fjerner linjeskift fra hver linje.
chomp($line);
}
# Henter ut hvert enkelt ord på hver linje.
my ($pred, $arg1, $arg2) = split(/,/, $line);
# Registerer arg1 og arg2 på predikatet (øker telleren).
$pred_args1{$pred}{$arg1} += 1;
$pred_args2{$pred}{$arg2} += 1;
# Registrerer predikatene som arg1 og arg2 forekommer med.
$arg1_preds{$arg1}{$pred} += 1;
$arg2_preds{$arg2}{$pred} += 1;
### Her er selve logikken i programmet ###
# Henter predikatene som skal vises fra kommandolinjen (viser alle hvis ingen er
oppgitt).
my @preds = scalar @ARGV ? @ARGV : sort keys %pred_args1;
# Løkke som styrer flyten i programmet.
foreach $pred (@preds) {
foreach $arg (1, 2) {
### NIVÅ 0 ###
my @args_lvl0 = parse_level0($arg, $pred);
}
foreach $arg_lvl0 (@args_lvl0) {
### NIVÅ 1 ###
my @args_lvl1 = parse_level1or2(1, $arg, $arg_lvl0, $pred);
foreach $arg_lvl1 (@args_lvl1) {
### NIVÅ 2 ###
parse_level1or2(2, $arg, $arg_lvl1, $pred, $arg_lvl0);
}
}
print "\n";
}
print "\n";
110

# Subrutine som tar inn argumentnummer (1 eller 2, tilsvarer ARG1 eller ARG2) og
predikat.
# Viser predikatets argumenter (NIVÅ0) og returnerer dem for bruk på neste nivå.
sub parse_level0 {
# Henter subrutinens parametre.
my $argnum = shift;
my $pred = shift;
# Henter ut alle predikatets argumenter.
my %args = $argnum == 1 ? %{$pred_args1{$pred}} : %{$pred_args2{$pred}};
# Henter ut selve argumentene i sortert rekkefølge (etter antall forekomster og
alfabetisk).
my @args = sort { $args{$b} $args{$a} } sort { lc($a) cmp lc($b) } keys %args;
}
# Viser predikatets argument-liste.
print "NIVÅ0, ARG$argnum ($pred): ";
print join(', ', map { "$_ x $args{$_}" } @args), "\n";
return @args;
# Subrutine som tar inn argumentnummer (1 eller 2, tilsvarer ARG1 eller ARG2) og et
argument (pluss ekstra styringsparametre).
# Finner andre predikater som argumentet er brukt i forbindelse med.
# Viser argumenter for alle funnede predikater, samt antallet ganger disse argumentene
ble brukt.
#
# I resten av subrutinen kalles disse funnede argumentene for "refererte argumenter (på
neste nivå)",
# fordi de indirekte via et predikat er referert fra et argument (eks: et NIVÅ0-argument
"refererer"
# til en del NIVÅ1-arguenter, som igjen referer til en del NIVÅ2-argumenter).
sub parse_level1or2 {
# Henter subrutinens parametre.
my $level = shift;
my $argnum = shift;
my $arg = shift;
my $pred_lvl0 = shift;
my $arg_lastlevel = $level == 1 ? '' : shift;
# Tar ikke hensyn til '?'-argumenter.
return if $arg eq '?';
# Datastruktur som teller antall refererte argumenter på neste nivå.
my %argrefs = ();
# Henter ut alle predikatene som inneholder argumentet.
my @preds = $argnum == 1 ? keys %{$arg1_preds{$arg}} : keys %{$arg2_preds{$arg}};
# ...og gjennomløper disse predikatene.
foreach $pred (@preds) {
# Tar ikke hensyn til samme predikatet som man holder på med.
next if $pred eq $pred_lvl0;
# Henter ut alle predikatets argumenter.
my %args = $argnum == 1 ? %{$pred_args1{$pred}} : %{$pred_args2{$pred}};
# ...og gjennomløper og teller disse.
foreach $argref (keys %args) {
# Tar ikke hensyn til samme argument som man holder på med.
next if $argref eq $arg || $argref eq $arg_lastlevel;
}
# Øker telleren (antall refererte argumenter på neste nivå).
$argrefs{$argref} += $args{$argref};
111

}
# Henter ut de refererte argumentene på neste nivå.
my @argrefs = sort { $argrefs{$b} $argrefs{$a} } sort { lc($a) cmp lc($b) } keys
%argrefs;
# Viser de refererte argumentene på neste nivå.
print " " x (3*$level), "NIVÅ$level, ARG$argnum ($arg): ";
print scalar @argrefs ? join(', ', map { "$_ x $argrefs{$_}" } @argrefs) : '(Ingen
referanser)', "\n";
}
return @argrefs;
112

Appendix F: POS-based structures
SENTENCE POS-STRUCTURE
Kvinne funnet død i Førde. finne,,kvinne
Den savnede kvinnen i Førde er nå funnet død. finne,kvinne,død
Politiet har gitt media opplysninger om funnet. gi,politi,opplysning
Lensmannen bekrefter at kvinnen er funnet død. bekrefte,lensmann,at
finne,kvinne,død
Politiet har bedt Kripos om bistand i søket etter kvinnen. be,politi,kripos
23-åringen var førsteårs sykepleiestudent i Førde. være,23-åring,
Hun møtte ikke opp til sin første praksisdag ved Førde
aldershjem.
møte,hun,
Politiet ble informert. informere,politi,
En leteaksjon ble satt igang. sette,leteaksjon,
Leteaksjonen pågikk til kvinnen ble funnet. pågå,leteaksjon,
finne,kvinne,
Politiet holder alle muligheter åpne i saken. holde,politi,mulighet
Etterforskerne vil ankomme i morgen. ankomme,etterforsker,
Et vitne hørte desperate rop om hjelp. høre,vitne,rop
Lensmannen har bedt om assistanse fra Kripos. be,lensmann,
Etterforskere fra Kripos skal bistå lensmannen i
etterforskningen.
bistå,etterforsker,lensmann
Etterforskerne forventes å ankomme i løpet av dagen. ankomme,etterforsker,
Den 23 år gamle studenten ble meldt savnet tidlig søndag
morgen.
melde savne,student,
Anne Slåtten bodde i et studentkollektiv i Førde. bo,Slåtten,
Slåtten var førsteårs sykepleiestudent i Førde. være,Slåtten,sykepleiestudent
Hun ble funnet omkommet i et skogholt. finne,hun,
Et vitne opplyste at hun hadde hørt høye rop. opplyse,vitne,
høre,hun,rop
Mandag holdt politiet en pressekonferanse. holde,politi,pressekonferanse
Lensmannen vil ikke gi nærmere opplysninger om åstedet. gi,lensmann,opplysning
Beboerne i studentkollektivet har fortalt politiet at de så fortelle,beboer,politi
Slåtten lørdag kveld.
se,de,
Politiet har sperret av åstedet. sperre,politi,
Flere personer er avhørt i saken. avhøre,person,
Politiet holder alle muligheter åpne. holde,politi,mulighet
Kvinnen blir trolig obdusert i løpet av tirsdag. obdusere,kvinne,
Politiet håper obduksjonen vil avklare hva som skjedde med håpe,politi,
kvinnen
avklare,obduksjon,hva
Mandag kveld ankom etterforskere fra Kripos åstedet. ankomme,etterforsker,åsted
Sent mandag kveld rigget etterforskerne opp lyskastere. rigge,etterforsker,
Fonn vil ikke gi flere opplysninger om åstedet. gi,Fonn,opplysning
Han vil ikke kommentere om kvinnen var skadet. kommentere,han,
skade,kvinne,
Politiet holder kortene svært tett til brystet. holde,politi,kort
Det er ikke kommet inn mange tips i saken. komme,det,
Tipsene skal nå systematiseres. systematisere,tips,
Fonn forteller at politiet vil ta kontakt med vitner. fortelle,Fonn,
ta,politi,kontakt
Politiet har flere mulige teorier. ha,politi,teori
Det mest sentrale vitnet i saken er en kvinne. være,vitne,kvinne
Hun skal ha hørt rop fra en kvinne. høre,hun,rop
Politiet har stengt av studentkollektivet der 23-åringen stenge,politi,
bodde.
bo,23-åring,
Studentkollektivet vil bli gjennomgått av teknikere. gjennomgå,studentkollektiv,
Fonn har bedt om teknisk bistand. be,Fonn,
Politiet bekrefter at Slåtten ble drept. bekrefte,politi,at
drepe,Slåtten,
Undersøkelsene på stedet viser at hun ble drept. vise,undersøkelse,at
drepe,hun,
Politiet tror at Slåtten ble overfalt. tro,politi,at
overfalle,Slåtten,
De tror at kvinnen ble drept av en ukjent gjerningsmann. tro,de,at
drepe,kvinne,
113

Politiet fastslår at kvinnens lommebok ikke er funnet. fastslå,politi,at
finne,lommebok,
Fonn opplyser at området ikke er undersøkt. opplyse,Fonn,at
undersøke,område,
Politiet har forkastet en tidligere teori. forkaste,politi,teori
Politiet får senere i dag svar på dødsårsaken. få,politi,svar
Politiet har ikke gjennomsøkt Slåttens studenthybel. gjennomsøke,politi,studenthybel
Hele hybelhuset ble sperret av. sperre,hybelhus,
Politiet har plombert hybelhuset. plombere,politi,hybelhus
Politiet skal finkjemme bygningen for tekniske spor. finkjemme,politi,bygning
De tekniske etterforskerne har undersøkt åstedet. undersøke,etterforsker,åsted
To tekniske etterforskere bistår politiet i Førde. bistå,etterforsker,politi
En taktisk etterforsker fra Kripos bistår politiet. bistå,etterforsker,politi
Lensmannen tar høyde for alle eventualiteter. ta,lensmann,høyde
Vi varslet Kripos. varsle,vi,kripos
Den døde sykepleierstudenten ble funnet av en tilfeldig
finne,sykepleierstudent,forbipasse
forbipasserende.
rende
23-åringen ble sist observert lørdag kveld. observere,23-åring,
Politiet vet at hun fikk en telefon fra kjæresten sin. vite,politi,
få,hun,telefon
Kjæresten merket ikke at noe var galt.
Vedkommende er avhørt.
merke,kjæreste,at
En større leteaksjon ble igangsatt. igangsette,leteaksjon,
Politiet etterlyser en syklist. etterlyse,politi,syklist
Den etterlyste syklisten har tatt kontakt med politiet. ta,syklist,kontakt
Fortsatt etterlyses to bilførere. etterlyse,,bilfører
Politiet etterlyste i dag to bilførere. etterlyse,politi,bilfører
To biler er observert på veien. observere,bil,
Politiet ønsker å komme i kontakt med bilførerne. ønske,politi,å
Fonn understreker at bilførerne er vitner. understreke,Fonn,at
være,bilfører,vitne
Fonn sier at han understreker dette. si,Fonn,at
understreke,han,dette
Slåtten var påkledd da hun ble funnet drept. være,Slåtten,påkledd
finne,hun,drepe
Vi vil nå kartlegge alle bevegelser på åstedet. kartlegge,vi,bevegelse
Vi har ingen spesiell teori som vi tar utgangspunkt i. ha,vi,teori
ta,vi,utgangspunkt
Funnene på åstedet viser at det er en kriminell handling. vise,funn,at
være,det,handling
Det er ikke et mistenkelig dødsfall, men en kriminell
handling.
være,det,dødsfall
Trenger flere vitner. trenge,vitne,
Politiet ønsker å komme i kontakt med alle som kjente
ønske,politi,å
Slåtten.
kjenne,,Slåtten
Etterforskerne fra Kripos vil kontakte vitner. kontakte,etterforsker,vitne
Politiet kjenner dødsårsaken. kjenne,politi,dødsårsak
Politimesteren bekrefter at de har fått en muntlig rapport. bekrefte,politimester,at
ha,de,rapport
Han understreker at politiet ikke vil gi informasjon om
understreke,han,at
dødsårsaken.
gi,politi,informasjon
Politiet har ikke bekreftet hvor kvinnen ble drept. bekrefte,politi,
drepe,kvinne,
Politiet har nå 32 medarbeidere som etterforsker drapet. ha,politi,medarbeider
etterforske,medarbeider,drap
Syklisten meldte seg. melde,syklist,seg
Den etterlyste syklisten har nå meldt seg til politiet i
Førde.
melde,syklist,seg
Fortsatt etterlyses to bilførere. etterlyse,bilfører,
Politiet etterlyste i dag tidlig en syklist. etterlyse,politi,syklist
I formiddag meldte syklisten seg til politiet. melde,syklist,seg
Jeg vil understreke at vi ønsker å komme i kontakt med både understreke,jeg,at
syklisten og bilførerne som vitner, sier Fonn.
ønske,vi,å
vitne,bilfører,
si,Fonn,
Vi vil nå kartlegge alle bevegelser på funnstedet og i
boligen.
kartlegge,vi,bevegelse
114

Vi har ingen spesiell teori som vi jobber utifra nå. ha,vi,teori
jobbe,vi,
Men funnene på åstedet viser at det er en kriminell handling, vise,funn,at
forteller Fonn.
være,det,handling
fortelle,Fonn,
I tillegg vil politiet gjøre en rundspørring rundt åstedet i
løpet av dagen.
gjøre,politi,rundspørring
Den endelige rapporten vil være klar på torsdag. være,rapport,klar
To Kripos-spesialister skal analysere alle tekniske spor i
Førde.
analysere,Kripos-spesialist,spor
De to sluttet seg til Førde-politiet i går. slutte,to,seg
Alt av mobiltelefontrafikk, overvåkingsfilmer og
minibankaktiviteter rundt drapstidspunktet skal undersøkes.
undersøke,minibankaktivitet,
Slik kan politiet undersøke aktiviteten i området
undersøke,politi,aktivitet
sykepleiestudenten ble funnet drept.
finne,sykepleierstudent,
Lensmann Kjell Fonn ber alle som var i sentrum om å melde be,Fonn,alle
seg.
melde,,seg
Han understreker at etterforskningen er svært bred. understreke,han,at
være,etterforskning,bred
Politiet har sanket inn videoer fra alle overvåkningskameraer
i Førde.
sanke,politi,
Polititjenestefolk går gjennom materialet. gå,polititjenestefolk,
Kameraene vil gi en indikasjon på aktiviteten i Førde i det
aktuelle tidsrommet.
gi,kamera,indikasjon
Gjerningsmannen gjemte seg i busker på åstedet. gjemme,gjerningsmann,seg
Det er sannsynlig at gjerningsmannen gjemte seg i busker ved være,det,sannsynlig
åstedet.
gjemme,gjerningsmann,seg
Tror Anne ble et tilfeldig offer. bli,Anne,offer
Politiet avhører flere vitner. avhøre,politi,vitne
Politiet har søkt med hunder på åstedet. søke,politi,
Politiet har samlet mange observasjoner. samle,politi,observasjon
Politiet antyder at drapsmannen har valgt sykepleiestudenten antyde,politi,at
tilfeldig.
velge,drapsmann,sykepleiestudent
Vitneavhør gir indikasjoner på at den brutale drapsmannen har gi,vitneavhør,indikasjon
valgt sykepleierstudenten tilfeldig.
velge,drapsmann,sykepleiestudent
Etterforskerne har flere observasjoner. ha,etterforsker,observasjon
Vitner så en kvinne som gikk alene. se,vitne,kvinne
Politiet mener kvinnen er Anne Slåtten. mene,politi,
være,Slåtten,kvinne
Etterforskerne mener at hun ikke ble forfulgt. mene,etterforsker,at
bli,hun,forfulgt
Drapsmannen kan ha gjemt seg i busker ved åstedet. gjemme,drapsmann,seg
Lensmannen ber unge kvinner være oppmerksomme. be,lensmann,kvinne
Politiet setter ikke inn ekstra patruljer i Førde. sette,politi,
Politiet har fått flere nye tips. ha,politi,tips
En rekonstruksjon ble gjennomført på tirsdag. gjennomføre,rekonstruksjon,
Lensmannen tror at politiet finner drapsmannen. tro,lensmann,at
finne,politi,drapsmann
Politiet etterlyser fem personer. etterlyse,politi,person
Personene er observert i Førde. observere,person,
Politiet har ikke identifisert dem. identifisere,politi,de
Politiet har gjort 1275 avhør. gjøre,politi,avhør
De har fem observasjoner av ukjente personer. ha,de,observasjon
115