HPSG Parsing with Shallow Dependency Constraints

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 624–631, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics HPSG Parsing with Shallow Dependency Constraints Kenji Sagae1 and Yusuke Miyao1 and Jun’ichi Tsujii1,2,3 1Department of Computer Science University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan 2School of Computer Science, University of Manchester 3National Center for Text Mining {sagae,yusuke,tsujii}@is.s.u-tokyo.ac.jp Abstract We present a novel framework that com- bines strengths from surface syntactic pars- ing and deep syntactic parsing to increase deep parsing accuracy, specifically by com- bining dependency and HPSG parsing. We show that by using surface dependencies to constrain the application of wide-coverage HPSG rules, we can benefit from a num- ber of parsing techniques designed for high- accuracy dependency parsing, while actu- ally performing deep syntactic analysis. Our framework results in a 1.4% absolute im- provement over a state-of-the-art approach for wide coverage HPSG parsing. 1 Introduction Several efficient, accurate and robust approaches to data-driven dependency parsing have been proposed recently (Nivre and Scholz, 2004; McDonald et al., 2005; Buchholz and Marsi, 2006) for syntactic anal- ysis of natural language using bilexical dependency relations (Eisner, 1996). Much of the appeal of these approaches is tied to the use of a simple formalism, which allows for the use of efficient parsing algo- rithms, as well as straightforward ways to train dis- criminative models to perform disambiguation. At the same time, there is growing interest in pars- ing with more sophisticated lexicalized grammar formalisms, such as Lexical Functional Grammar (LFG) (Bresnan, 1982), Lexicalized Tree Adjoin- ing Grammar (LTAG) (Schabes et al., 1988), Head- driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) and Combinatory Categorial Gram- mar (CCG) (Steedman, 2000), which represent deep syntactic structures that cannot be expressed in a shallower formalism designed to represent only as- pects of surface syntax, such as the dependency formalism used in current mainstream dependency parsing. We present a novel framework that combines strengths from surface syntactic parsing and deep syntactic parsing, specifically by combining depen- dency and HPSG parsing. We show that, by us- ing surface dependencies to constrain the applica- tion of wide-coverage HPSG rules, we can bene- fit from a number of parsing techniques designed for high-accuracy dependency parsing, while actu- ally performing deep syntactic analysis. From the point of view of HPSG parsing, accuracy can be im- proved significantly through the use of highly ac- curate discriminative dependency models, without the difficulties involved in adapting these models to a more complex and linguistically sophisticated formalism. In addition, improvements in depen- dency parsing accuracy are converted directly into improvements in HPSG parsing accuracy. From the point of view of dependency parsing, the applica- tion of HPSG rules to structures generated by a sur- face dependency model provides a principled and linguistically motivated way to identify deep syntac- tic phenomena, such as long-distance dependencies, raising and control. We begin by describing our dependency and HPSG parsing approaches in section 2. In section 3, we present our framework …

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