Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Zero Pronoun Identification in Chinese Language with Deep Neural Networks

Authors
Tao Chang, Shaohe Lv, Xiaodong Wang, Dong Wang
Corresponding Author
Tao Chang
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.116How to use a DOI?
Keywords
zero pronoun; Identification; LSTM
Abstract
Zero pronoun resolution is very important in natural language processing. Identification of zero pronoun is the fundamental task of its resolution. Existing feature engineering based identification approaches are unsuitable for big data applications due to labor-intensive work. Furthermore, as extracted from parse trees which are not unique for a certain sentence, features may be improper for zero pronoun identification. In this paper, we constructed a two-layer stacked bidirectional LSTM model to tackle identification of zero pronoun. To extract semantic knowledge, the first layer obtains the structure information of the sentence, and the second layer combines the part-of-speech information with obtained structure information. The results in two different kinds of experimental environment show that, our approach significantly outperforms the state-of-the-art method with an absolute improvement of 4.3% and 20.3% F-score in OntoNotes 5.0 corpus respectively.
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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.116How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Tao Chang
AU  - Shaohe Lv
AU  - Xiaodong Wang
AU  - Dong Wang
PY  - 2017/06
DA  - 2017/06
TI  - Zero Pronoun Identification in Chinese Language with Deep Neural Networks
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 518
EP  - 522
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.116
DO  - https://doi.org/10.2991/caai-17.2017.116
ID  - Chang2017/06
ER  -