Joint Syntactic and Semantic Parsing of Chinese

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1108–1117, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Joint Syntactic and Semantic Parsing of Chinese Junhui Li and Guodong Zhou School of Computer Science & Technology Soochow University Suzhou, China 215006 {lijunhui, gdzhou}@suda.edu.cn Hwee Tou Ng Department of Computer Science National University of Singapore 13 Computing Drive, Singapore 117417 nght@comp.nus.edu.sg Abstract This paper explores joint syntactic and seman- tic parsing of Chinese to further improve the performance of both syntactic and semantic parsing, in particular the performance of se- mantic parsing (in this paper, semantic role labeling). This is done from two levels. Firstly, an integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Secondly, semantic informa- tion generated by semantic parsing is incorpo- rated into the syntactic parsing model to better capture semantic information in syntactic parsing. Evaluation on Chinese TreeBank, Chinese PropBank, and Chinese NomBank shows that our integrated parsing approach outperforms the pipeline parsing approach on n-best parse trees, a natural extension of the widely used pipeline parsing approach on the top-best parse tree. Moreover, it shows that incorporating semantic role-related informa- tion into the syntactic parsing model signifi- cantly improves the performance of both syn- tactic parsing and semantic parsing. To our best knowledge, this is the first research on exploring syntactic parsing and semantic role labeling for both verbal and nominal predi- cates in an integrated way. 1 Introduction Semantic parsing maps a natural language sen- tence into a formal representation of its meaning. Due to the difficulty in deep semantic parsing, most previous work focuses on shallow semantic parsing, which assigns a simple structure (such as WHO did WHAT to WHOM, WHEN, WHERE, WHY, HOW) to each predicate in a sentence. In particular, the well-defined semantic role labeling (SRL) task has been drawing in- creasing attention in recent years due to its im- portance in natural language processing (NLP) applications, such as question answering (Nara- yanan and Harabagiu, 2004), information extrac- tion (Surdeanu et al., 2003), and co-reference resolution (Kong et al., 2009). Given a sentence and a predicate (either a verb or a noun) in the sentence, SRL recognizes and maps all the con- stituents in the sentence into their corresponding semantic arguments (roles) of the predicate. In both English and Chinese PropBank (Palmer et al., 2005; Xue and Palmer, 2003), and English and Chinese NomBank (Meyers et al., 2004; Xue, 2006), these semantic arguments include core arguments (e.g., Arg0 for agent and Arg1 for recipient) and adjunct arguments (e.g., ArgM-LOC for locative argument and ArgM-TMP for temporal argument). According to predicate type, SRL can be divided into SRL for verbal predicates (verbal SRL, in short) and SRL for nominal predicates (nominal SRL, in short). With the availability of large annotated cor- pora such as FrameNet (Baker et al., 1998), PropBank, and NomBank in English, data-driven techniques, including both feature-based and kernel-based methods, have been extensively studied for SRL (Carreras and Màrquez, …

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