A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A machine learning approach to pronoun resolution in spoken dialogue
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Multi-lingual coreference resolution with syntactic features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Disambiguating between generic and referential "you" in dialog
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Coreference Resolution on Blogs and Commented News
DAARC '09 Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium on Anaphora Processing and Applications
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In this paper, we propose the use of metadata contained in documents to improve coreference resolution. Specifically, we quantify the impact of speaker and turn information on the performance of our coreference system, and show that the metadata can be effectively encoded as features of a statistical resolution system, which leads to a statistically significant improvement in performance.