Unni Cathrine Eiken February 2005

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lensmann (deputy) Fonn politi (police) medarbeider (co-worker) politi (police) person (person) gjerningsmann (perpetrator) politi (police) lensmann (deputy) etterforsker (investigator) politimester (chief of police) politi (police) polititjenestefolk (police workers) politi (police) Slåtten Anne kvinne (woman) tekniker (technician) politi (police) 23-åring (23-year-old) kvinne (woman) Test 3 When using the overlap metric, all feature values are seen as equally dissimilar (Daelemans et al. 2003, p. 23). This means that the classifier is unable to determine the similarity of values such as politi (police), etterforsker (investigator) and politimester (chief of police) by means of looking at their co-occurrence with target classes. By using the Modified Value Difference Metric (MVDM), the features are weighted according to the patterns they occur in. Unfortunately, MVDM does not perform so well when used on small data sets with values that only occur a few times in the data set. When trained and tested on the EPAS_arg1 list, MVDM produced slightly lower accuracies than in the corresponding test with the overlap metric (see test 2 above). In practise, this meant that the benefits of MVDM could not be exploited due to the size of the data material. Test 4 Training set: EPAS_arg1 excluding structures with pronouns and structures with non-verbal predicates Test method: leave-one-out Result: 45,03% (68/151) correct classifications 66
The training and test material was modified by excluding all EPAS with non-verbal predicates, as well as all EPAS with the predicate være (be). This was done to see if these structures disturb the data material by adding irrelevant information that does not contribute to giving more information about the distribution of arguments in the EPAS. The accuracy increased slightly upon this modification of the data set. The editing did, however, not increase the accuracy when training on the edited EPAS_arg1 list and testing on EPAS containing pronouns in argument 1 position. 4.1.2.2 Classifying argument 2 Analogous to the classification steps performed for argument 1, the classifications were repeated for occurrences of argument 2. The EPAS list with the second argument as category label will in the following be referred to as EPAS_arg2. Test 1 Training set: EPAS_arg2 with no pronouns, argument 2 ignored. Test set: EPAS with pronouns in argument 2 position Result: (0/6) correct classifications Training the classifier on the EPAS_arg2 list without pronouns and testing on the EPAS with pronouns in argument 2 position did not produce any correct classifications. This is likely to in part be because of the small size of the test data set, as well as the homogenous nature of the test instances. Five of the wrongly classified instances were in fact of the same type; in all instances, the classifier had assigned the category kvinne (woman), while the correct antecedent was Slåtten. Test 2 Training set: EPAS_arg2 with no pronouns, argument 2 ignored Test set: leave-one-out Result: 49,73% (95/191) correct classifications 67
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University of Bergen Section for li
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Preface The project presented in th
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Table of Contents 1 INTRODUCTION AN
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1 Introduction and problem statemen
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patterns found in a text collection
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The results obtained in this projec
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The term anaphor describes a lingui
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2.1.1.1 Discourse representation th
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eferring to BT. The NP which is lin
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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
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: Test 2 Training set: EPAS_arg1 with
Page 75 and 76: • level 0: words which co-occur w
Page 77 and 78: (4- 9) avklare,obduksjon,? bede-om,
Page 79 and 80: (4-10) below shows the output for t
Page 81 and 82: In the introduction to this chapter
Page 83 and 84: the EPAS can be used in the classif
Page 85 and 86: exemption of jobbe-utfra, none of t
Page 87 and 88: antecedent for (4-15a). In the case
Page 89 and 90: Figure 7 Interestingly enough, howe
Page 91 and 92: When testing on knowledge-dependent
Page 93 and 94: Firth, J. R. (1957): A synopsis of
Page 95 and 96: Appendix A: Ekstraktor.pl - algorit
Page 97 and 98: finnARG2(); This function has exact
Page 99 and 100: #legger lest linje inn i @prt derso
Page 101 and 102: sub fjernEP{ #fjerner elementer fra
Page 103 and 104: } splice(@ARGx); $imax = @ARG3ep; @
Page 105 and 106: } else{ } } } push(@liste, $ARG0ep[
Page 107 and 108: 101 Appendix C: the EPAS list 23-å
Page 109 and 110: 103 obdusere,,kvinne observere,,23-
Page 111 and 112: Appendix D: Text aligned with EPAS
Page 113 and 114: eventualiteter. Vi varslet Kripos.
Page 115 and 116: Etterforskerne har flere observasjo
Page 117 and 118: # Subrutine som tar inn argumentnum
Page 119 and 120: Appendix F: POS-based structures SE
Page 121: Vi har ingen spesiell teori som vi
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Unni Cathrine Eiken February 2005

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