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Báo cáo khoa học: "Improving data-driven dependency parsing using large-scale LFG grammars"

This paper presents experiments which combine a grammar-driven and a datadriven parser. We show how the conversion of LFG output to dependency representation allows for a technique of parser stacking, whereby the output of the grammar-driven parser supplies features for a data-driven dependency parser. We evaluate on English and German and show significant improvements stemming from the proposed dependency structure as well as various other, deep linguistic features derived from the respective grammars. . | Improving data-driven dependency parsing using large-scale LFG grammars Lilja 0vrelid Jonas Kuhn and Kathrin Spreyer Department of Linguistics University of Potsdam lilja kuhn spreyer @ling.uni-potsdam.de Abstract This paper presents experiments which combine a grammar-driven and a data-driven parser. We show how the conversion of LFG output to dependency representation allows for a technique of parser stacking whereby the output of the grammar-driven parser supplies features for a data-driven dependency parser. We evaluate on English and German and show significant improvements stemming from the proposed dependency structure as well as various other deep linguistic features derived from the respective grammars. 1 Introduction The divide between grammar-driven and data-driven approaches to parsing has become less pronounced in recent years due to extensive work on robustness and efficiency for the grammar-driven approaches Riezler et al. 2002 Cahill et al. 2008b . The linguistic generalizations captured in such knowledge-based resources are thus increasingly available for use in practical applications. The NLP-community has in recent years witnessed a surge of interest in dependency-based approaches to syntactic parsing spurred by the CoNLL shared tasks of dependency parsing Buchholz and Marsi 2006 Nivre et al. 2007 . Nivre and McDonald 2008 show how two different approaches to dependency parsing the graphbased and transition-based approaches may be combined and subsequently learn to complement each other to achieve improved parse results for a range of different languages. In this paper we show how a data-driven dependency parser may straightforwardly be modified to learn directly from a grammar-driven parser. We evaluate on English and German and show significant improvements for both languages. Like Nivre and McDonald 2008 we supply a data-driven dependency parser with features from a different parser to guide parsing. The additional parser employed in this work