Deep Neural Architectures for Joint Named Entity Recognition and Disambiguation

Qianwen Wang, Mizuho Iwaihara

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Current entity linking methods typically first apply a named entity recognition (NER) model to extract a named entity and classify it into a predefined category, then apply an entity disambiguation model to link the named entity to a corresponding entity in the reference knowledge base. However, these methods ignore the inter-relations between the two tasks. Our work jointly optimizes deep neural models of both NER and entity disambiguation. In the entity disambiguation task, our deep neural model includes a recursive neural network and convolutional neural network with attention mechanism. Our model compares similarities between a mention and candidate entities by simultaneously considering semantic and background information, including Wikipedia description pages, contexts where the mention occurs, and entity typing information. The experiments show that our model can effectively leverage the semantic information of context, and performs competitively to conventional approaches.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538677896
DOI
出版ステータスPublished - 2019 4 1
イベント2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan
継続期間: 2019 2 272019 3 2

出版物シリーズ

名前2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
CountryJapan
CityKyoto
Period19/2/2719/3/2

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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