1. Anaphor Resolution in Norwegian Gordana Ilic Holen Institut for lingvistiske fag Det historisk-filosofiske fakultet Universitetet i Oslo g.i.holen@hfstud.uio.no

2. January 2003Fefor2 Some technical data zHovedfagsoppgave (incl. obligatory courses, a 4 semestrer project) zAim: Making a system for resolving pronominal anaphors in Norwegian. zMentor: Janne Bondi Johannessen zImplementation in (CLOS) LISP zTo be finished Christmas 2003

3. January 2003Fefor3 Where did it start? zMartin Hassel, 2000 yMade AR system for Swedish pronouns han/ honom/ hans and hon/ henne /hennes zDifferences yPlanning to cover more pronouns yA different theoretical background

4. January 2003Fefor4 The Top List zHan/ ham/ hans and hun/ henne/ hennes yAmong the most used; not ambiguous zSeg and selv ySyntactic solutions zDen yAmbiguous with the determinative den (gule bilen).

5. January 2003Fefor5 The Top Wish List zDe yAmbiguous with a determinative de (gule bilene) yProblems delimiting the antecedent z Det yProblems in deciding whether det is pronominal xdet (gule huset) xdet (regner)

6. January 2003Fefor6 Approach To be based on zMitkov's anaphora resolution system/ MARS (Mitkov 1996, 1998) and partially on zResolution of Anaphora Procedures/ RAP (Leass & Lappin 1994).

7. January 2003Fefor7 Why MARS and RAP zBoth made for English zMARS: intuitive, fully automated zRAP: high precision zFlexible

8. January 2003Fefor8 MARS zNo parsing zThe AR module uses a list of preferences called antecedent indicators yBoosting yImpeding zFully automatic, not very high precision (60 - 61%)

9. January 2003Fefor9 MARS: The algorithm zThe text is POS tagged. zNPs are extracted by a NP-extractor zNPs which precede the anaphor (in a two- sentence scope) are located zGender and number constraints are applied zAntecedent indicators are applied to the antecedent candidates that agree in gender and number. The scores (2, 1, 0 or -1) are assigned. zThe NP with the highest score is proposed as antecedent.

10. January 2003Fefor10 MARS: Antecedent indicators (boosting) zFirst noun phrases +1 z Indicating verbs +1 z Lexical reiteration +2 / +1 z Section heading preference +1 z Collocation match +2 z Immediate reference +2 z Sequential instructions +2 z Term preference +2

11. January 2003Fefor11 MARS: Antecedent indicators (boosting) zFirst noun phrases +1 z Indicating verbs +1 z Lexical reiteration +2 / +1 z Section heading preference +1 z Collocation match +2 z Immediate reference +2 z Sequential instructions +2 z Term preference +2

12. January 2003Fefor12 MARS: Antecedent indicators (boosting) zFirst noun phrases +1 z Indicating verbs +1 z Lexical reiteration +2 / +1 z Section heading preference +1 z Collocation match +2 z Immediate reference +2 z Sequential instructions +2 z Term preference +2

13. January 2003Fefor13 MARS: Antecedent indicators (impeding) zIndefiniteness -1 z Prepositional NPs -1

14. January 2003Fefor14 RAP zA high precision system (86% correctly resolved anaphors) zOriginally based on parsed text, but there exists a version without (Kennedy and Boguraev, 1996) zThe AR module: Salience weighting

15. January 2003Fefor15 RAP: Salience weighting zSalience factors: ySentence recency 100 ySubject emphasis80 yHead noun emphasis 80 yExistential emphasis 70 yAccusative emphasis 50 yNon-adverbial emphasis 50 yIO and oblique component emphasis 40

16. January 2003Fefor16 Modifications As both systems exist in versions with or without parsing, leaving this question open. Starting with using Oslo Corpus for training and adjusting zExperiment with antecedent indicators and adjust them for Norwegian zTry to combine them with RAP’s salience factors

17. January 2003Fefor17 Open for suggestions g.i.holen@hfstud.uio.no