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Parsing technology is concerned with finding syntactic structure in language. In parsing we have to deal with incomplete and not necessarily accurate formal descriptions of natural languages. Robustness and efficiency are among the main issuesin parsing. Corpora can be used to obtain frequency information about language use. This allows probabilistic parsing, an approach that aims at both robustness and efficiency increase. Approximation techniques, to be applied at the level of language description, parsing strategy, and syntactic representation, have the same objective. Approximation at the level of syntactic representation is also known as underspecification, a traditional technique to deal with syntactic ambiguity. In this book new parsing technologies are collected that aim at attacking the problems of robustness and efficiency by exactly these techniques: the design of probabilistic grammars and efficient probabilistic parsing algorithms, approximation techniques applied to grammars and parsers to increase parsing efficiency, and techniques for underspecification and the integration of semantic information in the syntactic analysis to deal with massive ambiguity. The book gives a state-of-the-art overview of current research and development in parsing technologies. In its chapters we see how probabilistic methods have entered the toolbox of computational linguistics in order to be applied in both parsing theory and parsing practice. The book is both a unique reference for researchers and an introduction to the field for interested graduate students.
List of Figures. List of Tables. Acknowledgements. 1. New Parsing Technologies; H. Bunt, A. Nijholt. 2. Encoding Frequency Information in Lexicalized Grammars; J. Carroll, D. Weir. 3. Bilexical Grammars and Their Cubic-Time Parsing Algorithms; J. Eisner. 4. Probabilistic Feature Grammars; J. Goodman. 5. Probabilistic GLR Parsing; K. Inui, et al. 6. Probabilistic Parsing Using Left Corner Language Models; C. Manning, B. Carpenter. 7. A New Parsing Method Using a Global Association Table; J. Yoon, et al. 8. Towards a Reduced Commitment, D-Theory Style TAG Parser; J. Chen, K. Vijay-Shanker. 9. Probabilistic Parse Selection Based on Semantic Co-occurrences; E. Hektoen. 10. Message-Passing Protocols for Object-Oriented Parsing; U. Hahn, et al. 11. SuperTagging for Partial Parsing. 12. Regular Approximation of CFLs: A Grammatical View; M.-J. Nederhof. 13. Parsing By Successive Approximation; H. Schmid. Index.