{"type":"ScholarlyArticle","title":"A Deep Neural Network for Chinese Zero Pronoun Resolution","authors":[{"name":"Qingyu Yin","slug":"qingyu-yin","given":"Qingyu","family":"Yin","affiliation":"Research Center for Social Computing and Information Retrieval Harbin Institute of Technology , China"},{"name":"Weinan Zhang","slug":"weinan-zhang","given":"Weinan","family":"Zhang","affiliation":"Research Center for Social Computing and Information Retrieval Harbin Institute of Technology , China"},{"name":"Yu Zhang","slug":"yu-zhang","given":"Yu","family":"Zhang","affiliation":"Research Center for Social Computing and Information Retrieval Harbin Institute of Technology , China"},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":"Research Center for Social Computing and Information Retrieval Harbin Institute of Technology , China"}],"abstract":"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.","doi":"10.24963/ijcai.2017/464","url":"https://ijcai.org/proceedings/2017/464","pdf":"https://ijcai.org/proceedings/2017/0464.pdf","markdown":"https://ijcai.org/proceedings/2017/464.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/464","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":464,"pages":{"first":3322,"last":3328},"keywords":["Machine Learning: Deep Learning","Natural Language Processing: Discourse","Natural Language Processing: Natural Language Processing","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2003] Yoshua Bengio, Rejean Ducharme, and Pascal Vincent. 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