Hedwig: A Named Entity Linker

2020-05-01 · LREC 2020 5 · Marcus Klang, Pierre Nugues

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{\%}.

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Entity Linking

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