Machine Learning for Coreference Resolution: From Local
Classification to Global Ranking
Vincent Ng.
43rd Annual Meeting of the Asssociation for Computational Linguistics (ACL), 2005.
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Abstract
In this paper, we view coreference resolution as a problem of
ranking candidate partitions generated by different coreference systems.
We propose a set of partition-based features to learn a ranking model
for distinguishing good and bad partitions.
Our approach compares favorably to two state-of-the-art coreference
systems when evaluated on three standard coreference data sets.