Dropped personal pronoun recovery in Chinese SMS*. (30th May 2017)

• Record Type: Journal Article
• Authors: GIANNELLA, CHRIS; WINDER, RANSOM; PETERSEN, STACY
• Is Part Of: Natural language engineering. Volume 23:Part 6(2017)
• Page Start: 905
• Page End: 927
• Publication Date: 2017-05-30
• Subjects: Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324917000158
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: In written Chinese, personal pronouns are commonly dropped when they can be inferred from context. This practice is particularly common in informal genres like Short Message Service messages sent via cell phones. Restoring dropped personal pronouns can be a useful preprocessing step for information extraction. Dropped personal pronoun recovery can be divided into two subtasks: (1) detecting dropped personal pronoun slots and (2) determining the identity of the pronoun for each slot. We address a simpler version of restoring dropped personal pronouns wherein only the person numbers are identified. After applying a word segmenter, we used a linear-chain conditional random field to predict which words were at the start of an independent clause. Then, using the independent clause start information, as well as lexical and syntactic information, we applied a conditional random field or a maximum-entropy classifier to predict whether a dropped personal pronoun immediately preceded each word and, if so, the person number of the dropped pronoun. We conducted a series of experiments using a manually annotated corpus of Chinese Short Message Service. Our approaches substantially outperformed a rule-based approach based partially on rules developed by Chung and Gildea (2010, Effects of Empty Categories on Machine Translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics. pp. 636–45). Our approaches also outperformed (though by a considerably smaller margin) a machine-learning approach based closely on work by Yang, Liu, and Xue in (2015, Recovering Dropped Pronouns from Chinese Text Messages. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL) . Association for Computational Linguistics. pp. 309–13). Features derived from parsing largely did not help our approaches. We conclude that, given independent clause start information, the parse information we used was largely superfluous for identifying dropped personal pronouns.