Fast and Accurate Neural Word Segmentation for Chinese

Deng Cai, Hai Zhao, Zhisong Zhang, Yuan Xin, Yongjian Wu, Feiyue Huang


Abstract
Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. In this paper, we propose a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.
Anthology ID:
P17-2096
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
608–615
Language:
URL:
https://aclanthology.org/P17-2096
DOI:
10.18653/v1/P17-2096
Bibkey:
Cite (ACL):
Deng Cai, Hai Zhao, Zhisong Zhang, Yuan Xin, Yongjian Wu, and Feiyue Huang. 2017. Fast and Accurate Neural Word Segmentation for Chinese. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 608–615, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Fast and Accurate Neural Word Segmentation for Chinese (Cai et al., ACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/P17-2096.pdf
Code
 jcyk/greedyCWS