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
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–108
Language:
URL:
https://www.aclweb.org/anthology/D19-5513
DOI:
10.18653/v1/D19-5513
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5513.pdf