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
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/D19-5513.pdf