A survey on recent advances in named entity recognition from deep learning models

Vikas Yadav, Steven Bethard

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

181 Scopus citations

Abstract

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

Original languageEnglish (US)
Title of host publicationCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
EditorsEmily M. Bender, Leon Derczynski, Pierre Isabelle
PublisherAssociation for Computational Linguistics (ACL)
Pages2145-2158
Number of pages14
ISBN (Electronic)9781948087506
StatePublished - 2018
Event27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States
Duration: Aug 20 2018Aug 26 2018

Publication series

NameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings

Conference

Conference27th International Conference on Computational Linguistics, COLING 2018
Country/TerritoryUnited States
CitySanta Fe
Period8/20/188/26/18

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

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