Unni Cathrine Eiken February 2005

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Mitkov, Ruslan. (2001): Outstanding issues in anaphora resolution. In: Alexander Gelbukh (ed): Computational Linguistics and Intelligent Text Processing, pp. 110-125 Mitkov, Ruslan. (2003): Anaphora Resolution. Chapter 14 in Mitkov (ed): The Oxford Handbook of Computational Linguistics. Oxford University Press, pp. 266-283. Nasukawa, Tetsuya. (1994): Robust method of pronoun resolution using full-text information. Proceedings of the 15 th International Conference on Computational Linguistics (COLING’94, Kyoto), pp.1157-1163. Available at: http://acl.eldoc.ub.rug.nl/mirror/C/C94/index.html NorGram website (2004): http://www.hf.uib.no/i/LiLi/SLF/Dyvik/norgram/ Consulted 23/11-2004 OBT (<strong>2005</strong>): Oslo-Bergen-taggeren Available at: http://decentius.aksis.uib.no/cl/cgp/obt.html Pantel, Patrick and Dekang Lin (2002): Discovering word senses from text. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Edmonton), pp. 613-619. Pereira, Fernando, N. Tishby, L. Lee. (1993): Distributional clustering of English words. Proceedings of the 31st Annual Meeting of the ACL, pp. 183-190. Available at: http://acl.eldoc.ub.rug.nl/mirror/P/P93/index.html Robbins, R.H. (1997): A Short History of Linguistics. Longman. Saeed, John I. (1997): Semantics. Blackwell. Velldal, Erik. (2003): Modelling Word Senses With Fuzzy Clustering. Cand. Philol. Thesis in Language, Logic and Information. University of Oslo. Wolff, Karl Erich. (1994): A first course in formal concept analysis. In: Faulbaum, F. (ed): SoftStat’93 Advances in Statistical Software 4, pp. 429-438. 88
Appendix A: Ekstraktor.pl – algorithm The algorithm behind Ekstraktor is divided into two separate parts: information retrieval from the Prolog file and processing of the information that was found and stored. First a Prolog output file is opened and each line of the file is read. Based on patternmatching, lines from the file are stored in different arrays according to which pattern they match. Subsequent to the information-extraction from the Prolog file, the information stored in the arrays is processed for the purpose of creating predicate-argument structures. In the following. I will give a brief outline of the processing steps. I will do this by describing each of the central functions in Ekstraktor. The term epmor (eng: ep mother) corresponds to the first EP in the ARG0ep-array, in most cases meaning the EP “in question”. finnHoved(); Finds the semantic forms of the main/first predicate-argument structure in the sentence. This function calls the following (sub)functions: finnEP1(); Since the entities parsed are full sentences, the main structures is limited to having a verb as its head. This function searches the array catsuff for a pattern with the first member of ARG0ep as its EP. If such a pattern is found, the EP is discarded and the first members of arrays ARG0ep and ARG0verdi are removed. finnPred(); Finds the semantic value of the sentence’s predicate/ARG0. Goes through the array semform searching for a pattern with the first member of ARG0ep as EP. If such a pattern is found, the semantic form is retrieved and stored in the array predikat. In order to avoid an “empty” semantic form if the argument is a proper noun, it is checked if the retrieved form matches named. If so, the array navn is searched for a pattern with the first member of ARG0ep as EP. If such an entry is found, predikat is emptied and the new semantic form is stored there. Some predicates have an extra attribute which is stored in the array prt. Each line in this array is searched for a pattern with the first member of ARG0ep as EP. If such an entry is found, the semantic form is retrieved and stored in the array ekstra. lagVerbStruktur(); Creates the correct verbal structure for the predicate. This is for the cases where the predicate has an additional attribute – as in the predicate “lete etter” (Eng: look for). The 89
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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
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(2- 12) REC SUBJ EXIST OBJ IND-OBJ
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Figure 1 17
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means that the algorithm would prop
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for an overview). Many of these sys
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(2- 15) a. Politiet etterlyste i da
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section. The theory dates back to t
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2.2.2 Different types of context So
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neighbours. For example, a target w
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with it. Selectional constraints al
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3 From text to EPAS - the extractio
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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
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: Firth, J. R. (1957): A synopsis of
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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