Abstract
Named entity recognition is a key component in various natural language processing systems, and neural architectures provide significant improvements over conventional approaches. Regardless of different word embedding and hidden layer structures of the networks, a conditional random field layer is commonly used for the output. This work proposes to use a neural language model as an alternative to the conditional random field layer, which is more flexible for the size of the corpus. Experimental results show that the proposed system has a significant advantage in terms of training speed, with a marginal performance degradation.- Anthology ID:
- 2020.coling-main.612
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6937–6941
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.612
- DOI:
- 10.18653/v1/2020.coling-main.612
- Cite (ACL):
- Zhihong Lei, Weiyue Wang, Christian Dugast, and Hermann Ney. 2020. Neural Language Modeling for Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6937–6941, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Neural Language Modeling for Named Entity Recognition (Lei et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.612.pdf