Nested Named Entity Recognition Revisited

Arzoo Katiyar, Claire Cardie


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.
Anthology ID:
N18-1079
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
861–871
Language:
URL:
https://aclanthology.org/N18-1079
DOI:
10.18653/v1/N18-1079
Bibkey:
Cite (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.
Cite (Informal):
Nested Named Entity Recognition Revisited (Katiyar & Cardie, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1079.pdf