Unni Cathrine Eiken February 2005

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(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
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: with it. Selectional constraints al
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
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Appendix F: POS-based structures SE
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Vi har ingen spesiell teori som vi
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Unni Cathrine Eiken February 2005

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