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Zero Pronoun Identification in Chinese Language with Deep Neural Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chang, Tao Lv, Shaohe Wang, Xiaodong Wang, Dong |
| Copyright Year | 2017 |
| 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. Keywords-zero pronoun; Identification; LSTM |
| File Format | PDF HTM / HTML |
| DOI | 10.2991/caai-17.2017.116 |
| Alternate Webpage(s) | https://download.atlantis-press.com/article/25881224.pdf |
| Alternate Webpage(s) | https://doi.org/10.2991/caai-17.2017.116 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
