Dependency Parsing and Projection Based on Word-Pair Classification

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 12–20, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Dependency Parsing and Projection Based on Word-Pair Classification Wenbin Jiang and Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704, Beijing 100190, China {jiangwenbin, liuqun}@ict.ac.cn Abstract In this paper we describe an intuitionistic method for dependency parsing, where a classifier is used to determine whether a pair of words forms a dependency edge. And we also propose an effective strategy for dependency projection, where the de- pendency relationships of the word pairs in the source language are projected to the word pairs of the target language, leading to a set of classification instances rather than a complete tree. Experiments show that, the classifier trained on the projected classification instances significantly out- performs previous projected dependency parsers. More importantly, when this clas- sifier is integrated into a maximum span- ning tree (MST) dependency parser, ob- vious improvement is obtained over the MST baseline. 1 Introduction Supervised dependency parsing achieves the state- of-the-art in recent years (McDonald et al., 2005a; McDonald and Pereira, 2006; Nivre et al., 2006). Since it is costly and difficult to build human- annotated treebanks, a lot of works have also been devoted to the utilization of unannotated text. For example, the unsupervised dependency parsing (Klein and Manning, 2004) which is totally based on unannotated data, and the semisupervised de- pendency parsing (Koo et al., 2008) which is based on both annotated and unannotated data. Considering the higher complexity and lower per- formance in unsupervised parsing, and the need of reliable priori knowledge in semisupervised pars- ing, it is a promising strategy to project the de- pendency structures from a resource-rich language to a resource-scarce one across a bilingual corpus (Hwa et al., 2002; Hwa et al., 2005; Ganchev et al., 2009; Smith and Eisner, 2009; Jiang et al., 2009). For dependency projection, the relationship be- tween words in the parsed sentences can be sim- ply projected across the word alignment to words in the unparsed sentences, according to the DCA assumption (Hwa et al., 2005). Such a projec- tion procedure suffers much from the word align- ment errors and syntactic isomerism between lan- guages, which usually lead to relationship projec- tion conflict and incomplete projected dependency structures. To tackle this problem, Hwa et al. (2005) use some filtering rules to reduce noise, and some hand-designed rules to handle language heterogeneity. Smith and Eisner (2009) perform dependency projection and annotation adaptation with quasi-synchronous grammar features. Jiang and Liu (2009) resort to a dynamic programming procedure to search for a completed projected tree. However, these strategies are all confined to the same category that dependency projection must produce completed projected trees. Because of the free translation, the syntactic isomerism between languages and word alignment errors, it would be strained to completely project the dependency structure from one language to another. We propose an effective method …

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