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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5513</identifier>
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%0 Conference Proceedings
%T Contextualized Word Representations from Distant Supervision with and for NER
%A Ghaddar, Abbas
%A Langlais, Phillippe
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F ghaddar-langlais-2019-contextualized
%X 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.
%R 10.18653/v1/D19-5513
%U https://aclanthology.org/D19-5513
%U https://doi.org/10.18653/v1/D19-5513
%P 101-108

Markdown (Informal)

[Contextualized Word Representations from Distant Supervision with and for NER](https://aclanthology.org/D19-5513) (Ghaddar & Langlais, EMNLP 2019)

• Contextualized Word Representations from Distant Supervision with and for NER (Ghaddar & Langlais, EMNLP 2019)

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.