Hedwig: A Named Entity Linker

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\%.

Tasks

Reproductions