Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art

Authors:
Veselin Stoyanov;Nathan Gilbert;Claire Cardie;Ellen Riloff
Affiliations:
Cornell University, Ithaca, NY;University of Utah, Salt Lake City, UT;Cornell University, Ithaca, NY;University of Utah, Salt Lake City, UT
Venue:
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Year:
2009

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Abstract

We aim to shed light on the state-of-the-art in NP coreference resolution by teasing apart the differences in the MUC and ACE task definitions, the assumptions made in evaluation methodologies, and inherent differences in text corpora. First, we examine three subproblems that play a role in coreference resolution: named entity recognition, anaphoricity determination, and coreference element detection. We measure the impact of each subproblem on coreference resolution and confirm that certain assumptions regarding these subproblems in the evaluation methodology can dramatically simplify the overall task. Second, we measure the performance of a state-of-the-art coreference resolver on several classes of anaphora and use these results to develop a quantitative measure for estimating coreference resolution performance on new data sets.