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    <abstract>Selectional preferences have long been claimed to be essential for coreference resolution. However, they are modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. Incorporating our model improves performance, matching state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile are such improvements.</abstract>
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%0 Conference Proceedings
%T Revisiting Selectional Preferences for Coreference Resolution
%A Heinzerling, Benjamin
%A Moosavi, Nafise Sadat
%A Strube, Michael
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F heinzerling-etal-2017-revisiting
%X Selectional preferences have long been claimed to be essential for coreference resolution. However, they are modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. Incorporating our model improves performance, matching state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile are such improvements.
%R 10.18653/v1/D17-1138
%U https://aclanthology.org/D17-1138/
%U https://doi.org/10.18653/v1/D17-1138
%P 1332-1339

Markdown (Informal)

[Revisiting Selectional Preferences for Coreference Resolution](https://aclanthology.org/D17-1138/) (Heinzerling et al., EMNLP 2017)

• Revisiting Selectional Preferences for Coreference Resolution (Heinzerling et al., EMNLP 2017)

ACL

• Benjamin Heinzerling, Nafise Sadat Moosavi, and Michael Strube. 2017. Revisiting Selectional Preferences for Coreference Resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1332–1339, Copenhagen, Denmark. Association for Computational Linguistics.