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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.722.pdf
Video:
 http://slideslive.com/38929323
Code
 neulab/soft-gazetteers
Data
CoNLL-2003