Word-Context Character Embeddings for Chinese Word Segmentation

Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, Xinyu Dai, Jiajun Chen


Abstract
Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data.
Anthology ID:
D17-1079
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
760–766
Language:
URL:
https://aclanthology.org/D17-1079
DOI:
10.18653/v1/D17-1079
Bibkey:
Cite (ACL):
Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2017. Word-Context Character Embeddings for Chinese Word Segmentation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 760–766, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Word-Context Character Embeddings for Chinese Word Segmentation (Zhou et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1079.pdf