Unni Cathrine Eiken February 2005

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upon processing of a new sentence. (2-10) below shows the weighting system of the salience factors in the system. (2- 10) SALIENCE FACTOR VALUE Sentence recency 100 Subject emphasis 80 Existential emphasis 70 Accusative emphasis 50 Indirect object or oblique complement emphasis 40 Non-adverbial emphasis 50 Head noun emphasis 80 In the following, this algorithm will be used in an attempt to resolve the referent for the anaphor han (he) in the second sentence of each member of the sentence pair presented in (1-1) and (1-2) and repeated in (2-11) below: (2- 11) a. Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig kommer til å drepe igjen. Han etterlyser vitner som var i sentrum søndag kveld. The sergeant leading the investigation says that the perpetrator probably will kill again. He puts out a call for witnesses who were in the city centre Sunday evening. b. Lensmannen som leder etterforskningen, sier at gjerningsmannen trolig kommer til å drepe igjen. Han er observert i sentrum. The sergeant leading the investigation says that the perpetrator probably will kill again. He is observed in the city centre. When attempting to find the antecedent for the pronoun in (2-11a), all potential referents must be collected. Since the discourse only consists of two sentences, only the first sentence is processed, in the event of a longer discourse the referent is looked for in until four preceding sentences. The potential referents lensmannen (the sergeant), etterforskningen (the investigation) and gjerningsmannen (the perpetrator) are assigned salience values as shown in (2-12) below. 14
(2- 12) REC SUBJ EXIST OBJ IND-OBJ NON-ADV HEAD N TOT lensmann 100 80 50 80 310 etterforskning 100 50 150 gjerningsmann 100 80 50 80 310 As the algorithm moves on to the next sentence, the values assigned in (2-12) are reduced by half, as shown in (2-13). (2- 13) Referent Phrases Value lensmann lensmannen 190 etterforskning etterforskningen 75 gjerningsmann gjerningsmannen 190 Now potential referents which do not agree in gender or number are removed. In our case, the pronoun is han (he), which for Norwegian bokmål specifies an animate referent. According to the preference factors in the algorithm, which check for gender and number only, the potential referent etterforskning cannot, however, be removed. At this stage, referents which do not satisfy intra-sentential syntactic coreference constraints will also be removed. Final salience values are calculated by assigning values for syntactic role parallelism and cataphora. In our case, lensmann is given extra weight due to the syntactic parallelism to the anaphor. This results in the values shown below: (2- 14) Referent Phrases Value lensmann lensmannen 225 etterforskning etterforskningen 75 gjerningsmann gjerningsmannen 190 Since lensmann has the highest salience weight, this word is also chosen as the referent for the pronoun han. Through the processing of sentence (2-11a) it is clear that the same referent would be assigned upon a processing of sentence (2-11b). The algorithm does not take the semantic meaning of the sentence to be processed into account, and is therefore not able to differ between the referent assignment in examples such as (2-11a) and (2-11b). 15
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: esolution system will not be able t
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
Page 69 and 70: . 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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