Unni Cathrine Eiken February 2005

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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. 4
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. 5
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: patterns found in a text collection
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 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
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value and highly desirable. As such
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this project, this can be interpret
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The process of classifying the cons
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There are several different distanc
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. ankomme,etterforsker,?,? ankomme,
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Test 2 Training set: EPAS_arg1 with
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The training and test material was
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• level 0: words which co-occur w
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(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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