Joint learning of the embedding of words and entities for named entity disambiguation

Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

112 Citations (Scopus)

Abstract

Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.

Original languageEnglish
Title of host publicationCoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages250-259
Number of pages10
ISBN (Electronic)9781945626197
DOIs
Publication statusPublished - 2016
Event20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016 - Berlin, Germany
Duration: 2016 Aug 112016 Aug 12

Publication series

NameCoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings

Conference

Conference20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016
CountryGermany
CityBerlin
Period16/8/1116/8/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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