Nested named entity recognition revisited

Arzoo Katiyar, Claire Cardie

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

34 Scopus citations

Abstract

We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-The-Art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages861-871
Number of pages11
ISBN (Electronic)9781948087278
StatePublished - 2018
Event2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
Duration: Jun 1 2018Jun 6 2018

Publication series

NameNAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
CountryUnited States
CityNew Orleans
Period6/1/186/6/18

All Science Journal Classification (ASJC) codes

  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

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