Multiple Character Embeddings for Chinese Word Segmentation

Jianing Zhou, Jingkang Wang, Gongshen Liu


Abstract
Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neural networks. However, these works neglect an important clue: Chinese characters incorporate both semantic and phonetic meanings. In this paper, we introduce multiple character embeddings including Pinyin Romanization and Wubi Input, both of which are easily accessible and effective in depicting semantics of characters. We propose a novel shared Bi-LSTM-CRF model to fuse linguistic features efficiently by sharing the LSTM network during the training procedure. Extensive experiments on five corpora show that extra embeddings help obtain a significant improvement in labeling accuracy. Specifically, we achieve the state-of-the-art performance in AS and CityU corpora with F1 scores of 96.9 and 97.3, respectively without leveraging any external lexical resources.
Anthology ID:
P19-2029
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
210–216
Language:
URL:
https://aclanthology.org/P19-2029
DOI:
10.18653/v1/P19-2029
Bibkey:
Cite (ACL):
Jianing Zhou, Jingkang Wang, and Gongshen Liu. 2019. Multiple Character Embeddings for Chinese Word Segmentation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 210–216, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Multiple Character Embeddings for Chinese Word Segmentation (Zhou et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-2029.pdf