@inproceedings{kantor-globerson-2019-coreference,
title = "Coreference Resolution with Entity Equalization",
author = "Kantor, Ben and
Globerson, Amir",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1066",
doi = "10.18653/v1/P19-1066",
pages = "673--677",
abstract = "A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an {``}Entity Equalization{''} mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6{\%}.",
}
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%0 Conference Proceedings
%T Coreference Resolution with Entity Equalization
%A Kantor, Ben
%A Globerson, Amir
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kantor-globerson-2019-coreference
%X A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an “Entity Equalization” mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6%.
%R 10.18653/v1/P19-1066
%U https://aclanthology.org/P19-1066
%U https://doi.org/10.18653/v1/P19-1066
%P 673-677
Markdown (Informal)
[Coreference Resolution with Entity Equalization](https://aclanthology.org/P19-1066) (Kantor & Globerson, ACL 2019)
ACL
- Ben Kantor and Amir Globerson. 2019. Coreference Resolution with Entity Equalization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 673–677, Florence, Italy. Association for Computational Linguistics.