New Models for Improving Supertag Disambiguation

Proceedings of EACL '99 New Models for Improving Supertag Disambiguation John Chen* Department of Computer and Information Sciences University of Delaware Newark, DE 19716 jchen@cis.udel.edu Srinivas Bangalore AT&T Labs Research 180 Park Avenue P.O. Box 971 Florham Park, NJ 07932 srini@research.att.com K. Vijay-Shanker Department of Computer and Information Sciences University of Delaware Newark, DE 19716 vijay~cis.udel.edu Abstract In previous work, supertag disambigua- tion has been presented as a robust, par- tial parsing technique. In this paper we present two approaches: contextual models, which exploit a variety of fea- tures in order to improve supertag per- formance, and class-based models, which assign sets of supertags to words in order to substantially improve accuracy with only a slight increase in ambiguity. 1 Introduction Many natural language applications are beginning to exploit some underlying structure of the lan- guage. Roukos (1996) and Jurafsky et al. (1995) use structure-based language models in the context of speech applications. Grishman (1995) and Hobbs et al. (1995) use phrasal information in information extraction. Alshawi (1996) uses dependency information in a machine translation system. The need to impose structure leads to the need to have robust parsers. There have been two main robust parsing paradigms: Fi- nite State Grammar-based approaches (such as Abney (1990), Grishman (1995), and Hobbs et al. (1997)) and Statistical Parsing (such as Charniak (1996), Magerman (1995), and Collins (1996)). Srinivas (1997a) has presented a different ap- proach called supertagging that integrates linguis- tically motivated lexical descriptions with the ro- bustness of statistical techniques. The idea un- derlying the approach is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (Su- pertags) that impose complex constraints in a lo- cal context. Supertag disambiguation is resolved "Supported by NSF grants ~SBR-9710411 and ~GER-9354869 by using statistical distributions of supertag co- occurrences collected from a corpus of parses. It results in a representation that is effectively a parse (almost parse). Supertagging has been found useful for a num- ber of applications. For instance, it can be used to speed up conventional chart parsers be- cause it reduces the ambiguity which a parser must face, as described in Srinivas (1997a). Chandrasekhar and Srinivas (1997) has shown that supertagging may be employed in informa- tion retrieval. Furthermore, given a sentence aligned parallel corpus of two languages and al- most parse information for the sentences of one of the languages, one can rapidly develop a gram- mar for the other language using supertagging, as suggested by Bangalore (1998). In contrast to the aforementioned work in su- pertag disambiguation, where the objective was to provide a-direct comparison between trigram models for part-of-speech tagging and supertag- ging, in this paper our goal is to improve the per- formance of supertagging using local techniques which avoid full parsing. These supertag disam- biguation models can be grouped into contextual models and class based models. Contextual mod- els use different features in frameworks that ex- ploit the information those features provide in order to achieve higher accuracies …

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