TAILIEUCHUNG - Báo cáo khoa học: " New Models for Improving Supertag Disambiguation"

In previous work, supertag disambiguation has been presented as a robust, partial parsing technique. In this paper we present two approaches: contextual models, which exploit a variety of features in order to improve supertag performance, and class-based models, which assign sets of supertags to words in order to substantially improve accuracy with only a slight increase in ambiguity. | 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@ Srinivas Bangalore AT T Labs Research 180 Park Avenue . Box 971 Florham Park NJ 07932 sr ini@research. att. com K. Vijay-Shanker Department of Computer and Information Sciences University of Delaware Newark DE 19716 vijay@ Abstract In previous work supertag disambiguation has been presented as a robust partial parsing technique. In this paper we present two approaches contextual models which exploit a variety of features in order to improve supertag performance 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 language. 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 Finite 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 approach called supertagging that integrates linguistically motivated lexical descriptions with the robustness of statistical techniques. The idea underlying the approach is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions Supertags that impose complex constraints in a local context. Supertag disambiguation is resolved Supported by NSF grants SBR-9710411 and GER-9354869 by using .

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