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Detecting Anaphoricity and Antecedenthood for Coreference Resolution

Detecting Anaphoricity and Antecedenthood for Coreference Resolution

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Detecting Anaphoricity and Antecedenthood for Coreference Resolution

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  1. Detecting Anaphoricity and Antecedenthood for Coreference Resolution Olga Uryupina (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

  13. Results: antecedenthood

  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

  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!