Contextualized Word Representations from Distant Supervision with and for NER

Abbas Ghaddar, Phillippe Langlais


Abstract
We describe a special type of deep contextualized word representation that is learned from distant supervision annotations and dedicated to named entity recognition. Our extensive experiments on 7 datasets show systematic gains across all domains over strong baselines, and demonstrate that our representation is complementary to previously proposed embeddings. We report new state-of-the-art results on CONLL and ONTONOTES datasets.
Anthology ID:
D19-5513
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–108
Language:
URL:
https://aclanthology.org/D19-5513
DOI:
10.18653/v1/D19-5513
Bibkey:
Cite (ACL):
Abbas Ghaddar and Phillippe Langlais. 2019. Contextualized Word Representations from Distant Supervision with and for NER. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 101–108, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Contextualized Word Representations from Distant Supervision with and for NER (Ghaddar & Langlais, WNUT 2019)
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PDF:
https://aclanthology.org/D19-5513.pdf