Unni Cathrine Eiken February 2005

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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
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: for an overview). Many of these sys
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 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,
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(4-10) below shows the output for t
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In the introduction to this chapter
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the EPAS can be used in the classif
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exemption of jobbe-utfra, none of t
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antecedent for (4-15a). In the case
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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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