Modern coreference resolution systems require linguistic and general knowledge typically sourced from costly, manually curated resources. Despite their intuitive appeal, results have been mixed. In this work, we instead implement fine-grained surface-level features motivated by cognitive theory. Our novel fine-grained feature specialisation approach significantly improves the performance of a strong baseline, achieving state-of-the-art results of 65.29 and 61.13% on CoNLL-2012 using gold and automatic preprocessing, with system extracted mentions.
CITATION STYLE
Webster, K., & Nothman, J. (2016). Using mention accessibility to improve coreference resolution. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers (pp. 432–437). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p16-2070
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