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        <title>Hierarchical Region Learning for Nested Named Entity Recognition</title>
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        <namePart type="given">Xinwei</namePart>
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    <name type="personal">
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            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-the-art baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.430</identifier>
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%0 Conference Proceedings
%T Hierarchical Region Learning for Nested Named Entity Recognition
%A Long, Xinwei
%A Niu, Shuzi
%A Li, Yucheng
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F long-etal-2020-hierarchical
%X Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-the-art baselines.
%R 10.18653/v1/2020.findings-emnlp.430
%U https://aclanthology.org/2020.findings-emnlp.430
%U https://doi.org/10.18653/v1/2020.findings-emnlp.430
%P 4788-4793

Markdown (Informal)

[Hierarchical Region Learning for Nested Named Entity Recognition](https://aclanthology.org/2020.findings-emnlp.430) (Long et al., Findings 2020)

• Hierarchical Region Learning for Nested Named Entity Recognition (Long et al., Findings 2020)

ACL

• Xinwei Long, Shuzi Niu, and Yucheng Li. 2020. Hierarchical Region Learning for Nested Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4788–4793, Online. Association for Computational Linguistics.