@inproceedings{fitzgerald-etal-2021-moleman,
title = "{MOLEMAN}: Mention-Only Linking of Entities with a Mention Annotation Network",
author = "FitzGerald, Nicholas and
Bikel, Dan and
Botha, Jan and
Gillick, Daniel and
Kwiatkowski, Tom and
McCallum, Andrew",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.37",
doi = "10.18653/v1/2021.acl-short.37",
pages = "278--285",
abstract = "We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as {``}class prototypes{''} as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor{'}s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.",
}
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<abstract>We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as “class prototypes” as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor’s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.</abstract>
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%0 Conference Proceedings
%T MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network
%A FitzGerald, Nicholas
%A Bikel, Dan
%A Botha, Jan
%A Gillick, Daniel
%A Kwiatkowski, Tom
%A McCallum, Andrew
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F fitzgerald-etal-2021-moleman
%X We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as “class prototypes” as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor’s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.
%R 10.18653/v1/2021.acl-short.37
%U https://aclanthology.org/2021.acl-short.37
%U https://doi.org/10.18653/v1/2021.acl-short.37
%P 278-285
Markdown (Informal)
[MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://aclanthology.org/2021.acl-short.37) (FitzGerald et al., ACL-IJCNLP 2021)
ACL
- Nicholas FitzGerald, Dan Bikel, Jan Botha, Daniel Gillick, Tom Kwiatkowski, and Andrew McCallum. 2021. MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 278–285, Online. Association for Computational Linguistics.