Machine Learning for Coreference Resolution: From Local Classification to Global Ranking
نویسنده
چکیده
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.
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