@inproceedings{katiyar-cardie-2018-nested,
title = "Nested Named Entity Recognition Revisited",
author = "Katiyar, Arzoo and
Cardie, Claire",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1079",
doi = "10.18653/v1/N18-1079",
pages = "861--871",
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.",
}
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%0 Conference Proceedings
%T Nested Named Entity Recognition Revisited
%A Katiyar, Arzoo
%A Cardie, Claire
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F katiyar-cardie-2018-nested
%X 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.
%R 10.18653/v1/N18-1079
%U https://aclanthology.org/N18-1079
%U https://doi.org/10.18653/v1/N18-1079
%P 861-871
Markdown (Informal)
[Nested Named Entity Recognition Revisited](https://aclanthology.org/N18-1079) (Katiyar & Cardie, NAACL 2018)
ACL
- Arzoo Katiyar and Claire Cardie. 2018. Nested Named Entity Recognition Revisited. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 861–871, New Orleans, Louisiana. Association for Computational Linguistics.