Soft Gazetteers for Low-Resource Named Entity Recognition
Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime Carbonell
Abstract
Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of “soft gazetteers” that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.- Anthology ID:
- 2020.acl-main.722
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8118–8123
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.722
- DOI:
- 10.18653/v1/2020.acl-main.722
- Cite (ACL):
- Shruti Rijhwani, Shuyan Zhou, Graham Neubig, and Jaime Carbonell. 2020. Soft Gazetteers for Low-Resource Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8118–8123, Online. Association for Computational Linguistics.
- Cite (Informal):
- Soft Gazetteers for Low-Resource Named Entity Recognition (Rijhwani et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.722.pdf
- Code
- neulab/soft-gazetteers
- Data
- CoNLL-2003