Natural Language Parsing: Psychological, Computational, and Theoretical Perspectives

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Overview

This is a collection of new papers by leading researchers on natural language parsing. In the past, the problem of how people parse the sentences they hear - determine the identity of the words in these sentences and group these words into larger units - has been addressed in very different ways by experimental psychologists, by theoretical linguists, and by researchers in artificial intelligence, with little apparent relationship among the solutions proposed by each group. However, because of important advances in all these disciplines, research on parsing in each of these fields now seems to have something significant to contribute to the others, as this volume demonstrates. The volume includes some papers applying the results of experimental psychological studies of parsing to linguistic theory, others which present computational models of parsing, and a mathematical linguistics paper on tree-adjoining grammars and parsing.

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Table of Contents

Introduction Laurie Karttunen and Arnold M. Zwicky; 1. Measuring syntactic complexity relative to discourse context Alice Davison and Richard Lutz; 2. Interpreting questions Elisabet Engdahl; 3. How can grammars help parsers? Stephen Crain and Janet Dean Fodor; 4. Syntactic complexity Lyn Frazier; 5. Processing of sentences with intrasentential code switching Aravind K. Joshi; 6. Tree adjoining grammars: how much context-sensitivity is required to provide reasonable structural descriptions Aravind K. Joshi; 7. Parsing in functional unification grammar Martin Kay; 8. Parsing in a free word order language Lauri Karttunen and Martin Kay; 9. A new characterization of attachment preferences Fernando C. N. Pereira; 10. On not being led up the garden path: the use of context by the pscyhological syntax processor Stephen Crain and Mark Steedman; 11. Do listeners compute linguistic representations? Michael K. Tanenhaus, Greg N. Carlson and Mark S. Seidenberg; Notes; References; Index.

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