@inproceedings{klang-nugues-2020-hedwig,
title = "{H}edwig: A Named Entity Linker",
author = "Klang, Marcus and
Nugues, Pierre",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.554",
pages = "4501--4508",
abstract = "Named entity linking is the task of identifying mentions of named things in text, such as {``}Barack Obama{''} or {``}New York{''}, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9{\%} on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9{\%}.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.</abstract>
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%0 Conference Proceedings
%T Hedwig: A Named Entity Linker
%A Klang, Marcus
%A Nugues, Pierre
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F klang-nugues-2020-hedwig
%X Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.
%U https://aclanthology.org/2020.lrec-1.554
%P 4501-4508
Markdown (Informal)
[Hedwig: A Named Entity Linker](https://aclanthology.org/2020.lrec-1.554) (Klang & Nugues, LREC 2020)
ACL
- Marcus Klang and Pierre Nugues. 2020. Hedwig: A Named Entity Linker. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4501–4508, Marseille, France. European Language Resources Association.