Zero Pronoun Identification in Chinese Language with Deep Neural Networks
Tao Chang, Shaohe Lv, Xiaodong Wang, Dong Wang
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.116How to use a DOI?
- zero pronoun; Identification; LSTM
- 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.
- 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 -