Unni Cathrine Eiken February 2005

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• nominal anaphora: The anaphor has a non-pronominal noun phrase as antecedent. o direct: anaphor and antecedent are linked through identity, synonymy, generalisation or specialisation. o indirect: anaphor and antecedent are linked through part-of-relations or set membership. He states that pronominal anaphora is the most frequent type, while nominal anaphora (indirect anaphora in particular) usually requires real-world knowledge to be resolved. In this thesis, the described method will be tested using occurrences of pronominal anaphora from the text collection. 2.1.1 Frameworks for anaphora resolution Anaphora resolution is the process of determining the antecedent of an anaphor (Mitkov 2003, p. 269). In our minds, we build a discourse model that represents the entities mentioned in the discourse and shows the relationship between them (Webber 1978, in Jurafsky and Martin 2000). A representation is evoked into the model upon the first mentioning in the discourse, and then subsequently accessed from the model if the entity is mentioned again, either by name or by way of anaphora. Entities will have varying degrees of salience in the discourse model, depending on how frequently they have been mentioned and also on how long ago they last were mentioned. This notion of a discourse model is used both in theories which aim at describing the process of anaphora resolution, and in computational approaches that automate the anaphora resolution process. There are different approaches to resolving referring expressions and anaphors that occur in natural language discourse. Several formalisms offer frameworks describing the theory of discourse representation in general and anaphora binding in particular. In the following sections, two of these formalisms will be briefly outlined. 8
2.1.1.1 Discourse representation theory Discourse representation theory (DRT), proposed by Hans Kamp in 1981, represents a way of creating dynamic semantic representations for natural language discourse. The framework aims at representing larger linguistic units than sentences and is particularly useful for representing the way discourse changes with every new sentence that is introduced. The core structure within DRT is the discourse representation structure (DRS), which is transformed through the processing of each sentence of a discourse. Since every sentence in a discourse potentially can introduce new concepts and entities which are referentially linked to previously introduced entities, it is not possible to infer the full meaning of an individual sentence without regarding the discourse it fits into. Kamp and Reyle states it as “the meaning of the whole is more, one might say, than the conjunction of its parts” (Kamp and Reyle 1993, p. 59). The interpretation of a new sentence relies both on the meaning of the sentence itself and on the structure representing the context of the earlier sentences (Kamp and Reyle 1993, p. 59). Thus, a new sentence is interpreted as a contribution to the existing representation of the discourse. DRT establishes anaphoric links across sentence boundaries between anaphors in the current sentence and antecedents in the DRS as the new sentence is processed. A very simplified outline of what happens when the discourse in (2-2) is processed and a DRS is created, is given below (after Kamp and Reyle 1993). (2- 2) a. Jones owns Ulysses. b. It fascinates him. (2-2a) is entered into the DRS by way of applying phrase structure rules and lexical insertion rules in order to associate the sentence with a syntactic representation and a set of features and values for the individual words. Some examples of assigned features are number, gender and transitiveness (for verbs). The DRS for (2-2a) will in abridged form look like this: 9
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: The term anaphor describes a lingui
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
Page 61 and 62: value and highly desirable. As such
Page 63 and 64: 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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