Abstract
Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities. In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models. We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.- Anthology ID:
- P19-1524
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5301–5307
- Language:
- URL:
- https://aclanthology.org/P19-1524
- DOI:
- 10.18653/v1/P19-1524
- Cite (ACL):
- Tianyu Liu, Jin-Ge Yao, and Chin-Yew Lin. 2019. Towards Improving Neural Named Entity Recognition with Gazetteers. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5301–5307, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Towards Improving Neural Named Entity Recognition with Gazetteers (Liu et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/P19-1524.pdf
- Code
- lyutyuh/acl19_subtagger
- Data
- CoNLL-2003, OntoNotes 5.0