Unni Cathrine Eiken February 2005

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(2- 8) The sergeant’s suspect hurt himself. Hellan (1988) suggests that the principles of standard Government and Binding theory are primarily based on English and that they cover “a very limited subpart if what constitutes a possible anaphoric system” (Hellan 1988, preface). He proposes several additional principles, maybe most notably the Command Principle in which, among other statements pertaining to the command relation, it is stated that also relations within hierarchies of thematic roles can stand in a command relation to an anaphor. 2.1.2 Computational approaches to anaphora resolution Automated anaphora resolution systems basically have to perform three separate tasks (Mitkov 2003): • identify the anaphors to be resolved • locate the candidates for antecedents • select the antecedent from the candidate list Different computational approaches apply different resolution factors and knowledge sources. The process of resolving the antecedent is based on several resolution factors, which in turn draw into account quite different sources of background knowledge. Using morphological knowledge may be the simplest approach; gender and/or number is compared and candidates are discounted if their gender/number does not fit that of the anaphor. Syntactic knowledge is used to identify syntactic parallelism; the antecedent is often found in a similar syntactic position as the anaphor. In many cases, the correct antecedent cannot be identified without the help of semantic information. Selectional constraints is one example of semantic knowledge that can be used to narrow down the list of candidates for the antecedent. Repeated mention of an entity in the preceding text passage to the anaphor may indicate that this entity has a higher degree of salience in the discourse and that it therefore is a likely antecedent for a following anaphor (Jurafsky and Martin 2000, p. 682). Using morphological, lexical, syntactic, semantic and salience criteria as background knowledge does not immediately suggest the most likely candidate, but rather acts as filters to eliminate unsuitable candidates (Mitkov 2003, p. 271). Mitkov (2003, p. 272) states that for some examples “the crucial and most reliable factor in deciding on the antecedent” is real-world knowledge. Even the most exquisite anaphora 12
esolution system will not be able to resolve anaphora of the type that needs real-world knowledge to rule out candidates that just do not make common sense. The examples in (2-9) below illustrate the point; without access to real-world knowledge or semantics, there is no way to confidently resolve the antecedent of the anaphoric han (he). (2- 9) a. Politimannen skjøt etter morderen, og han falt. The policeman shot at the murderer and he fell. b. Politimannen skjøt etter morderen, og han bommet. The policeman shot at the murderer and he missed. 2.1.2.1 Knowledge-free approaches Botley and McEnery term anaphora resolution systems which do not consult any form of knowledge representation in the process of identifying the antecedent of an anaphor “knowledge-free” (Botley and McEnery 2000, p. 17). In the two following sections, it will be shown, on the basis of two well-established syntactic algorithms for anaphora resolution, that knowledge-free approaches that resolve anaphors without employing real-world knowledge cannot identify different antecedents in the case of examples (1-1) and (1-2). 2.1.2.1.1 Lappin and Leass’ algorithm for pronoun resolution Lappin and Leass (Lappin and Leass 1994, in Jurafsky and Martin 2000) offer an algorithm for pronoun interpretation which takes into account recency and syntactically-based preferences. The algorithm does not employ semantic preferences or background knowledge, but uses a weighting system which reflects various syntactic features as well as salience of recency in the discourse. When testing this algorithm on test data from the same genre as was used to develop the weighting system, Lappin and Leass report an accuracy of 86%. Jurafsky and Martin present a somewhat simplified part of the algorithm in the resolution of non-reflexive, third-person pronouns (Jurafsky and Martin 2000, p. 684). The Lappin and Leass algorithm creates a discourse model upon processing a sentence and assigns each member of the discourse model a salience value. A set of salience factors determine the salience weight each of the members is assigned. The aspect of recency is maintained by reducing each member’s salience value by half 13
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: eferring to BT. The NP which is lin
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
Page 65 and 66: The process of classifying the cons
Page 67 and 68: 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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