Paper: Unsupervised Coreference Resolution by Utilizing the Most Informative Relations

ACL ID C14-1061
Title Unsupervised Coreference Resolution by Utilizing the Most Informative Relations
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper we present a novel method for unsupervised coreference resolution. We introduce a precision-oriented inference method that scores a candidate entity of a mention based on the most informative mention pair relation between the given mention entity pair. We introduce an infor- mativeness score for determining the most precise relation of a mention entity pair regarding the coreference decisions. The informativeness score is learned robustly during few iterations of the expectation maximization algorithm. The proposed unsupervised system outperforms existing unsupervised methods on all benchmark data sets.