Unni Cathrine Eiken February 2005

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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
Page 1 and 2: University of Bergen Section for li
Page 3 and 4: Preface The project presented in th
Page 5 and 6: Table of Contents 1 INTRODUCTION AN
Page 7 and 8: 1 Introduction and problem statemen
Page 9 and 10: patterns found in a text collection
Page 11 and 12: The results obtained in this projec
Page 13 and 14: The term anaphor describes a lingui
Page 15 and 16: 2.1.1.1 Discourse representation th
Page 17 and 18: eferring to BT. The NP which is lin
Page 19 and 20: esolution system will not be able t
Page 21 and 22: (2- 12) REC SUBJ EXIST OBJ IND-OBJ
Page 23 and 24: Figure 1 17
Page 25 and 26: means that the algorithm would prop
Page 27 and 28: for an overview). Many of these sys
Page 29 and 30: (2- 15) a. Politiet etterlyste i da
Page 31 and 32: section. The theory dates back to t
Page 33: 2.2.2 Different types of context So
Page 37 and 38: with it. Selectional constraints al
Page 39 and 40: 3 From text to EPAS - the extractio
Page 41 and 42: 3.2 Predicate-argument structures "
Page 43 and 44: speaker flexibility with regards to
Page 45 and 46: and woman occur together both in su
Page 47 and 48: occur with. Arguments which are unl
Page 49 and 50: 3.3.1 NorGram in outline Norsk komp
Page 51 and 52: Figure 3 The most useful structure
Page 53 and 54: 3.4 Altering the source As already
Page 55 and 56: (3- 12) (3- 13) Politiet leter ette
Page 57 and 58: ARG1 and ARG2 arrays display a valu
Page 59 and 60: (3- 20) Anne Slåtten bodde i et st
Page 61 and 62: value and highly desirable. As such
Page 63 and 64: this project, this can be interpret
Page 65 and 66: The process of classifying the cons
Page 67 and 68: There are several different distanc
Page 69 and 70: . ankomme,etterforsker,?,? ankomme,
Page 71 and 72: Test 2 Training set: EPAS_arg1 with
Page 73 and 74: The training and test material was
Page 75 and 76: • level 0: words which co-occur w
Page 77 and 78: (4- 9) avklare,obduksjon,? bede-om,
Page 79 and 80: (4-10) below shows the output for t
Page 81 and 82: In the introduction to this chapter
Page 83 and 84: the EPAS can be used in the classif
Page 85 and 86:
exemption of jobbe-utfra, none of t
Page 87 and 88:
antecedent for (4-15a). In the case
Page 89 and 90:
Figure 7 Interestingly enough, howe
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When testing on knowledge-dependent
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Firth, J. R. (1957): A synopsis of
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Appendix A: Ekstraktor.pl - algorit
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finnARG2(); This function has exact
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#legger lest linje inn i @prt derso
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sub fjernEP{ #fjerner elementer fra
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} splice(@ARGx); $imax = @ARG3ep; @
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} else{ } } } push(@liste, $ARG0ep[
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101 Appendix C: the EPAS list 23-å
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103 obdusere,,kvinne observere,,23-
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Appendix D: Text aligned with EPAS
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eventualiteter. Vi varslet Kripos.
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Etterforskerne har flere observasjo
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# Subrutine som tar inn argumentnum
Page 119 and 120:
Appendix F: POS-based structures SE
Page 121:
Vi har ingen spesiell teori som vi
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Unni Cathrine Eiken February 2005

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