New Developments in Parsing Technology -

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Résumé :
Parsing can be defined as the decomposition of complex structures into their constituent parts, and parsing technology as the methods, the tools, and the software to parse automatically. Parsing is a central area of research in the automatic processing of human language. Parsers are being used in many application areas, for example question answering, extraction of information from text, speech recognition and understanding, and machine translation. New developments in parsing technology are thus widely applicable. This book contains contributions from many of today's leading researchers in the area of natural language parsing technology. The contributors describe their most recent work and a diverse range of techniques and results. This collection provides an excellent picture of the current state of affairs in this area. This volume is the third in a series of such collections, and its breadth of coverage should make it suitable both as an overview of the current state ofthe field for graduate students, and as a reference for established researchers.

Sommaire:
Developments in Parsing Technology: From Theory to Application.- Parameter Estimation for Statistical Parsing Models: Theory and Practice of Distribution-Free Methods.- High Precision Extraction of Grammatical Relations.- Automated Extraction of Tags from the Penn Treebank.- Computing the Most Probable Parse for a Discontinuous Phrase Structure Grammar.- A Neural Netword Parser that Handles Sparse Data.- An Efficient LR Parser Generator for Tree-Adjoining Grammars.- Relating Tabular Parsing Algorithms for LIG and TAG.- Improved Left-Corner Chart Parsing for Large Context-Free Grammars.- On Two Classes of Feature Paths in Large-Scale Unification Grammars.- A Context-Free Superset Approximation of Unification-Based Grammars.- A Recognizer for Minimalist Languages.- Range Concatenation Grammars.- Grammar Induction by MDL-Based Distributional Classification.- Optimal Ambiguity Packing in Context-Free Parsers with Interleaved Unification.- Robust Data Oriented Spoken Language Understanding.- Soup: A Parser for Real-World Spontaneous Speech.- Parsing and Hypergraphs.- Measure for Measure: Towards Increased Component Comparability and Exchange.