Multi-lingual Dependency Parsing at NAIST

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 191–195, New York City, June 2006. c⃝2006 Association for Computational Linguistics Multi-lingual Dependency Parsing at NAIST Yuchang CHENG, Masayuki ASAHARA and Yuji MATSUMOTO Nara Institute of Science and Technology 8916-5 Takayama, Ikoma, Nara 630-0192, Japan {yuchan-c, masayu-a, matsu}@is.naist.jp Abstract In this paper, we present a framework for multi-lingual dependency parsing. Our bottom-up deterministic parser adopts Nivre’s algorithm (Nivre, 2004) with a preprocessor. Support Vector Machines (SVMs) are utilized to determine the word dependency attachments. Then, a maxi- mum entropy method (MaxEnt) is used for determining the label of the depend- ency relation. To improve the perform- ance of the parser, we construct a tagger based on SVMs to find neighboring at- tachment as a preprocessor. Experimental evaluation shows that the proposed exten- sion improves the parsing accuracy of our base parser in 9 languages. (Hajič et al., 2004; Simov et al., 2005; Simov and Osenova, 2003; Chen et al., 2003; Böh- mová et al., 2003; Kromann, 2003; van der Beek et al., 2002; Brants et al., 2002; Kawata and Bartels, 2000; Afonso et al., 2002; Džeroski et al., 2006; Civit and Martí, 2002; Nilsson et al., 2005; Oflazer et al., 2003; Atalay et al., 2003). 1 Introduction The presented dependency parser is based on our preceding work (Cheng, 2005a) for Chinese. The parser is a bottom-up deterministic dependency parser based on the algorithm proposed by (Nivre, 2004). A dependency attachment matrix is con- structed, in which each element corresponds to a pair of tokens. Each dependency attachment is in- crementally constructed, with no crossing con- straint. In the parser, SVMs (Vapnik, 1998) deterministically estimate whether a pair of words has either of four relations: right, left, shift and reduce. While dependency attachment is estimated by SVMs, we use a MaxEnt (Ratnaparkhi, 1999) based tagger with the output of the parser to esti- mate the label of dependency relations. This tagger uses the same features as for the word dependency analysis. In our preceding work (Cheng, 2005a), we not only adopted the Nivre algorithm with SVMs, but also tried some preprocessing methods. We inves- tigated several preprocessing methods on a Chi- nese Treebank. In this shared task (Buchholz et. al, 2006), we also investigate which preprocessing method is effective on other languages. We found that only the method that uses a tagger to extract the word dependency attachment between two neighboring words works effectively in most of the languages. 2 System Description The main part of our dependency parser is based on Nivre’s algorithm (Nivre, 2004), in which the dependency relations are constructed by a bottom- up deterministic schema. While Nivre’s method uses memory-based learning to estimate the de- pendency attachment and the label, we use SVMs to estimate the attachment and MaxEnt to estimate Fig. 1 The architecture of our parser (i)Preprocessor (neighboring relation tagger) (ii)Get contextual features (iii)Estimate dependency attachment by SVM (iv)Tag label by MaxEnt Construct Subtree No more construction Dependency tree False True Left or …

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