Named Entity Recognition With Parallel Recurrent Neural Networks

Andrej Žukov-Gregorič, Yoram Bachrach, Sam Coope


Abstract
We present a new architecture for named entity recognition. Our model employs multiple independent bidirectional LSTM units across the same input and promotes diversity among them by employing an inter-model regularization term. By distributing computation across multiple smaller LSTMs we find a significant reduction in the total number of parameters. We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.
Anthology ID:
P18-2012
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
69–74
Language:
URL:
https://aclanthology.org/P18-2012
DOI:
10.18653/v1/P18-2012
Bibkey:
Cite (ACL):
Andrej Žukov-Gregorič, Yoram Bachrach, and Sam Coope. 2018. Named Entity Recognition With Parallel Recurrent Neural Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 69–74, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Named Entity Recognition With Parallel Recurrent Neural Networks (Žukov-Gregorič et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2012.pdf
Poster:
 P18-2012.Poster.pdf