Abstract
The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.- Anthology ID:
- D19-1396
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3830–3840
- Language:
- URL:
- https://aclanthology.org/D19-1396
- DOI:
- 10.18653/v1/D19-1396
- Cite (ACL):
- Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, and Shengping Liu. 2019. Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3830–3840, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network (Sui et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/D19-1396.pdf
- Code
- DianboWork/Graph4CNER
- Data
- Weibo NER