A Stacked Sub-Word Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1385–1394, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics A Stacked Sub-Word Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging Weiwei Sun Department of Computational Linguistics, Saarland University German Research Center for Artificial Intelligence (DFKI) D-66123, Saarbr¨ucken, Germany wsun@coli.uni-saarland.de Abstract The large combined search space of joint word segmentation and Part-of-Speech (POS) tag- ging makes efficient decoding very hard. As a result, effective high order features represent- ing rich contexts are inconvenient to use. In this work, we propose a novel stacked sub- word model for this task, concerning both ef- ficiency and effectiveness. Our solution is a two step process. First, one word-based segmenter, one character-based segmenter and one local character classifier are trained to pro- duce coarse segmentation and POS informa- tion. Second, the outputs of the three pre- dictors are merged into sub-word sequences, which are further bracketed and labeled with POS tags by a fine-grained sub-word tag- ger. The coarse-to-fine search scheme is effi- cient, while in the sub-word tagging step rich contextual features can be approximately de- rived. Evaluation on the Penn Chinese Tree- bank shows that our model yields improve- ments over the best system reported in the lit- erature. 1 Introduction Word segmentation and part-of-speech (POS) tag- ging are necessary initial steps for more advanced Chinese language processing tasks, such as pars- ing and semantic role labeling. Joint approaches that resolve the two tasks simultaneously have re- ceived much attention in recent research. Previous work has shown that joint solutions led to accu- racy improvements over pipelined systems by avoid- ing segmentation error propagation and exploiting POS information to help segmentation. A challenge for joint approaches is the large combined search space, which makes efficient decoding and struc- tured learning of parameters very hard. Moreover, the representation ability of models is limited since using rich contextual word features makes the search intractable. To overcome such efficiency and effec- tiveness limitations, the approximate inference and reranking techniques have been explored in previous work (Zhang and Clark, 2010; Jiang et al., 2008b). In this paper, we present an effective and effi- cient solution for joint Chinese word segmentation and POS tagging. Our work is motivated by several characteristics of this problem. First of all, a major- ity of words are easy to identify in the segmentation problem. For example, a simple maximum match- ing segmenter can achieve an f-score of about 90. We will show that it is possible to improve the ef- ficiency and accuracy by using different strategies for different words. Second, segmenters designed with different views have complementary strength. We argue that the agreements and disagreements of different solvers can be used to construct an inter- mediate sub-word structure for joint segmentation and tagging. Since the sub-words are large enough in practice, the decoding for POS tagging over sub- words is efficient. Finally, the Chinese language is characterized by the lack of morphology that often provides …

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