An Encoding Strategy Based Word-Character LSTM for Chinese NER

Wei Liu, Tongge Xu, Qinghua Xu, Jiayu Song, Yueran Zu


Abstract
A recently proposed lattice model has demonstrated that words in character sequence can provide rich word boundary information for character-based Chinese NER model. In this model, word information is integrated into a shortcut path between the start and the end characters of the word. However, the existence of shortcut path may cause the model to degenerate into a partial word-based model, which will suffer from word segmentation errors. Furthermore, the lattice model can not be trained in batches due to its DAG structure. In this paper, we propose a novel word-character LSTM(WC-LSTM) model to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information. Four different strategies are explored in our model to encode word information into a fixed-sized representation for efficient batch training. Experiments on benchmark datasets show that our proposed model outperforms other state-of-the-arts models.
Anthology ID:
N19-1247
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2379–2389
Language:
URL:
https://aclanthology.org/N19-1247
DOI:
10.18653/v1/N19-1247
Bibkey:
Cite (ACL):
Wei Liu, Tongge Xu, Qinghua Xu, Jiayu Song, and Yueran Zu. 2019. An Encoding Strategy Based Word-Character LSTM for Chinese NER. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2379–2389, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
An Encoding Strategy Based Word-Character LSTM for Chinese NER (Liu et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1247.pdf
Supplementary:
 N19-1247.Supplementary.zip
Code
 liuwei1206/CCW-NER
Data
Weibo NER