A Deep Neural Network for Chinese Zero Pronoun Resolution
A Deep Neural Network for Chinese Zero Pronoun Resolution
Qingyu Yin, Weinan Zhang, Yu Zhang, Ting Liu
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3322-3328.
https://doi.org/10.24963/ijcai.2017/464
Existing approaches for Chinese zero pronoun resolution overlook semantic information. This is because zero pronouns have no descriptive information, which results in difficulty in explicitly capturing their semantic similarities with antecedents. Moreover, when dealing with candidate antecedents, traditional systems simply take advantage of the local information of a single candidate antecedent while failing to consider the underlying information provided by the other candidates from a global perspective. To address these weaknesses, we propose a novel zero pronoun-specific neural network, which is capable of representing zero pronouns by utilizing the contextual information at the semantic level. In addition, when dealing with candidate antecedents, a two-level candidate encoder is employed to explicitly capture both the local and global information of candidate antecedents. We conduct experiments on the Chinese portion of the OntoNotes 5.0 corpus. Experimental results show that our approach substantially outperforms the state-of-the-art method in various experimental settings.
Keywords:
Machine Learning: Neural Networks
Natural Language Processing: Discourse
Natural Language Processing: Natural Language Processing
Machine Learning: Deep Learning