Unni Cathrine Eiken February 2005

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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
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 and 34: 2.2.2 Different types of context So
Page 35 and 36: neighbours. For example, a target w
Page 37 and 38: with it. Selectional constraints al
Page 39 and 40: 3 From text to EPAS - the extractio
Page 41: 3.2 Predicate-argument structures "
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
Page 91 and 92: When testing on knowledge-dependent
Page 93 and 94:
Firth, J. R. (1957): A synopsis of
Page 95 and 96:
Appendix A: Ekstraktor.pl - algorit
Page 97 and 98:
finnARG2(); This function has exact
Page 99 and 100:
#legger lest linje inn i @prt derso
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sub fjernEP{ #fjerner elementer fra
Page 103 and 104:
} splice(@ARGx); $imax = @ARG3ep; @
Page 105 and 106:
} else{ } } } push(@liste, $ARG0ep[
Page 107 and 108:
101 Appendix C: the EPAS list 23-å
Page 109 and 110:
103 obdusere,,kvinne observere,,23-
Page 111 and 112:
Appendix D: Text aligned with EPAS
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eventualiteter. Vi varslet Kripos.
Page 115 and 116:
Etterforskerne har flere observasjo
Page 117 and 118:
# 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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