Abstract
Clustering step in the mention-pair paradigm for coreference resolution, forms the chain of coreferent mentions from the mention pairs classified as coreferent. Clustering methods including best-first clustering considers each antecedent candidate individually, while selecting the antecedent for an anaphoric mention. Here we introduce an easy-to-implement modification to best-first clustering to improve coreference resolution on Indian classical music forums. This method considers the relation between the candidate antecedents along with the relation between the anaphoric mention and the candidate antecedent. We observe a modest but statistically significant improvement over the best-first clustering for this dataset.
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Ross, J.C., Bhattacharyya, P. (2018). Improved Best-First Clustering for Coreference Resolution in Indian Classical Music Forums. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_18
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DOI: https://doi.org/10.1007/978-3-319-77113-7_18
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