1. Detecting Anaphoricity and Antecedenthood for Coreference Resolution Olga Uryupina (uryupina@gmail.com)uryupina@gmail.com Institute of Linguistics, RAS 13.11.08

2. Overview Anaphoricity and Antecedenthood Experiments Incorporating A&A detectors into a CR system Conclusion

3. A&A: example Shares in Loral Space will be distributed to Loral shareholders. The new company will start life with no debt and $700 million in cash. Globalstar still needs to raise $600 million, and Schwartz said that the company would try to raise the money in the debt market.

4. A&A: example Shares in Loral Space will be distributed to Loral shareholders. The new company will start life with no debt and $700 million in cash. Globalstar still needs to raise $600 million, and Schwartz said that the company would try to raise the money in the debt market.

5. Anaphoricity Likely anaphors: - pronouns, definite descriptions Unlikely anaphors: - indefinites Unknown: - proper names Poesio&Vieira: more than 50% of definite descriptions in a newswire text are not anaphoric!

6. A&A: example Shares in Loral Space will be distributed to Loral shareholders. The new company will start life with no debt and $700 million in cash. Globalstar still needs to raise $600 million, and Schwartz said that the company would try to raise the money in the debt market.

7. A&A: example Shares in Loral Space will be distributed to Loral shareholders. The new company will start life with no debt and $700 million in cash. Globalstar still needs to raise $600 million, and Schwartz said that the company would try to raise the money in the debt market.

8. Antecedenthood Related to referentiality (Karttunen, 1976): „no debt“ etc Antecedenthood vs. Referentiality: corpus-based decision

9. Experiments Can we learn anaphoricity/antecedenthood classifiers? Do they help for coreference resolution?

10. Methodology MUC-7 dataset Anaphoricity/antecedenthood induced from the MUC annotations Ripper, SVM

11. Features Surface form (12) Syntax (20) Semantics (3) Salience (10) „same-head“ (2) From Karttunen, 1976 (7) 49 features – 123 boolean/continuous

12. Results: anaphoricity Feature groupsRPF Baseline10066.579.9 All93.582.387.6 Surface10066.579.9 Syntax97.472.082.8 Semantics98.568.981.1 Salience91.269.378.7 Same-head84.581.182.8 Karttunen‘s91.671.180.1 Synt+SH90.083.586.6

13. Results: antecedenthood Feature groupsRPF Baseline10066.579.9 All95.769.280.4 Surface94.668.579.5 Syntax95.769.280.3 Semantics94.969.480.2 Salience98.967.079.9 Same-head10066.579.9 Karttunen‘s99.367.380.2

14. Integrating A&A into a CR system Apply an A&A prefiltering before CR starts: -Saves time -Improves precision Problem: we can filter out good candidates..: - Will loose some recall

15. Oracle-based A&A prefiltering Take MUC-based A&A classifier („gold standard“ CR system: Soon et al. (2001) with SVMs MUC-7 validation set (3 „training“ documents)

16. Oracle-based A&A prefiltering RPF No prefilteing54.556.955.7 ±ana49.673.659.3 ±ante54.269.460.9 ±ana & ±ante52.981.964.3

17. Automatically induced classifiers Precision more crucial than Recall Learn Ripper classifiers with different Ls (Loss Ratio)

18. Anaphoricity prefiltering

19. Antecedenthood prefiltering

20. Conclusion Automatically induced detectors: Reliable for anaphoricity Much less reliable for antecedenthood (a corpus, explicitly annotated for referentiality could help) A&A prefiltering: Ideally, should help In practice – substantial optimization required

21. Thank You!