Unni Cathrine Eiken February 2005

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Figure 2 In the process of identifying the antecedent for the pronoun han in the second sentence of (2-11a), the algorithm takes as its starting point the NP immediately dominating the pronoun. From there, it moves upwards in the tree to the first NP or sentence node. This node is called X and the path from the pronoun to X is called p. In our case, that means that X is the topmost sentence node (IP node). Since there are no more branches to the left of X and p, and in other words no possible antecedents introduced earlier in the same sentence, the algorithm moves along to the parse tree of the previous sentence. Searching left-to-right and breadth-first, the first NP node that is encountered is suggested as the antecedent for the pronoun. In our case, this 18
means that the algorithm would propose the NP lensmannen som leder etterforskningen as the antecedent for the pronoun han. As will be clear from examining the example sentences in (2-11), this is a correct resolution of the antecedent in (2-11a), but not for the antecedent in (2-11b). Parallel to the Lappin and Leass resolution algorithm, the tree search algorithm also does not consider the semantic meaning of the sentence with the anaphor to be resolved. The pronouns han in the second sentences of (2-11a) and (2-11b) are treated in the same way, and lensmannen is chosen as the most likely antecedent in both cases. 2.1.2.2 Traditional approaches to anaphora resolution As seen through the examples above, anaphors of the type that requires semantic information to be resolved simply cannot be resolved using purely syntactic algorithms. In order to find the antecedent for such anaphors, some sort of real-world knowledge must be consulted. Mitkov (1999) distinguishes between traditional and alternative approaches for anaphora resolution. The traditional approaches are those that use knowledge factors to filter out unlikely candidates and then use preference rules on a smaller set of likely candidates, while the alternative approaches find the most likely candidate based on statistical or AI techniques (Mitkov 1999, p. 8). The traditional approaches usually draw in the factor of real-world or domain knowledge, often in the form of a comprehensive knowledge or domain base, in order to resolve anaphors of the type in examples (2-9) and (2-11) above (Mitkov 2003). Such approaches are also called knowledge-based (Botley and McEnery 2000, p. 11). In the above it has repeatedly been emphasized that some types of anaphors cannot be correctly resolved without access to realworld information. Carbonell and Brown’s (1988) multi-strategy approach is one traditional knowledge-based anaphora resolution system. Their approach follows what Botley and McEnery call “a trend […] towards the integration of several different resolution algorithms into large-scale modular architectures” (Botley and McEnery 2000, p. 17). Their system draws on different knowledge sources, including syntactic structure, case-frame semantics, dialog structure and real-world knowledge. The resolution of anaphors is based on constraints and preferences; first the constraints are applied to narrow down the list of potential antecedents and then the preferences are applied to each of the remaining candidates (Carbonell and Brown 1988, p. 98). Real-world knowledge is realised as a set of precondition and postcondition constraints. These constraints for example determine that the object given no longer is in the possession of the actor after a successful act of giving has been carried out. The main problem 19
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: Figure 1 17
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
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
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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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