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New developments in parsing technology (Text, speech & language technology, Vol. 23)

Langue : Anglais

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Couverture de l’ouvrage New developments in parsing technology (Text, speech & language technology, Vol. 23)
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 line of such collections, and its breadth of coverage should make it suitable both as an overview of the current state of the field for graduate students, and as a reference for established researchers. This volume is of specific interest to researchers, advanced undergraduate students, graduate students, and teachers in the following areas: Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. It will also be of interest to designers, developers, and advanced users of natural language processing software and systems, including applications such as machine translation, information extraction, spoken dialogue, multimodal human-computer interaction, text mining, and semantic web technology.
Preface. 1: Developments in Parsing Technology: From Theory to Application, H. Bunt, J. Carroll, G. Satta Introduction About this book. 2: Parameter Estimation for Statistical Parsing Models: Theory and Practice of Distribution-Free Methods, M. Collins Introduction Linear Models Probabilistic Context-Free Grammars Statistical Learning Theory Convergence Bounds for Finite Sets of Hypotheses Convergence Bounds for Hyperplane Classifiers Application of Margin Analysis to Parsing Algorithms Discussion Conclusions. 3: High Precision Extraction of Grammatical Relations, J. Carroll, T. Briscoe Introduction The Analysis System Empirical Results Conclusions and Further Work. 4: Automated Extraction of TAGs from the Penn Treebank, J. Chen, K.V. Shanker Introduction Tree Extraction Procedure Evaluation Extended Extracted Grammars Related Work Conclusions. 5: Computing the Most Probable Parse for a Discontinuous Phrase-Structure Grammar, O. Plaehn Introduction Discontinuous Phrase-Structure Grammar The Parsing Algorithm Computing the Most Probable Parse Experiments Conclusion and Future Work. 6: A Neural Network Parser that Handles Sparse Data, J. Henderson Introduction Simple Synchrony Networks A Probabilistic Parser for SSNs Estimating the Probabilities with a Simple Synchrony Network Generalizing from Sparse Data Conclusion. 7: An Efficient LR Parser Generator for Tree-Adjoining Grammars, C.A. Prolo Introduction TAGS On Some Degenerate LR Models for TAGS Proposed Algorithm Implementation Example Some Properties Of the Algorithms Evaluation Conclusions. 8: Relating Tabular Parsing Algorithms for LIG and TAG, M.A. Alonso, E. de la Clergerie, V.J. Diaz, M. Vilares Introduction Tree-Adjoining Grammars Linear Indexed Grammars Bottom-up Parsing Algorithms Barley-like Parsing Algorithms Barley-like Parsing Algorithms Preserving the Correct Prefix Property Bidirectional Parsing Specialized TAG parsers Conclusion. 9: Improved Left-Corner Chart Parsing for Large Context-Free Grammars, R.C. Moore Introduction Evaluating Parsing Algorithms Terminology and Notation Test Grammars Left-Corner Parsing Algorithms and Refinements Grammar Transformations Extracting Parses from the Chart Comparison to Other Algorithms Conclusions. 10: On Two Classes of Feature Paths in Large-Scale Unification Grammars, L. Ciortuz Introduction Compiling the Quick Check Filter Generalised Rule Reduction Conclusion. 11: A Context-Free Superset Approximation of Unification-Based Grammars, B. Kiefer, H.-U. Krieger Introduction Basic Inventory Approximation as Fixpoint Construction The Basic Algorithm Implementation Issues and Optimizations Revisiting the Fixpoint Construction Three Grammars Disambiguation of UBGs via Probabilistic Approximations. 12: A Recognizer for Minimalist Languages, H. Harkema Introduction Minimalist Grammars Specification of the Recognizer Correctness Complexity Results Conclusions and Future Work. 13: Range Concatenation Grammars, P. Boullier Introduction Positive Range Concatenation Grammars Negative Range Concatenation Grammars A Parsing Algorithm for RCGs Closure Properties and Modularity Conclusion. 14: Grammar Induction by MDL-Based Distributional Classification, Yikun Guo, Fuliang Weng, Lide Wu Introduction Grammar Induction with the MDL Principle Induction Strategies MDL Induction by Dynamic Distributional Classification (DCC) Comparison and Conclusion. Appendix. 15: Optimal Ambiguity Packing in Context-Free Parsers with Interleaved Unification, A. Lavie, C. Penstein Rose Introduction Ambiguity Packing in Context Free Parsing The Rule Prioritization Heuristic Empirical Evaluations and Discussion Conclusions and Future Directions. 16: Robust Data-Oriented Spoken Language Understanding, K. Sima'an Introduction Brief Overview of OVIS OP vs. Tree-Gram Application to the OVIS Domain Conclusions. 17: SOUP: A Parser for Real-World Spontaneous Speech, M. Gavalda Introduction Grammar Representation Sketch of the Parsing Algorithm Performance Key Features Conclusion. 18: Parsing and Hypergraphs, D. Klein, C.D. Manning Introduction Hypergraphs and Parsing Viterbi Parsing Algorithm Analysis Conclusion. Appendix. 19: Measure for Measure: Towards Increased Component Comparability and Exchange, S. Oepen, U. Callmeier Competence & Performance Profiling Strong Empiricism: A Few Examples PET - Synthesizing Current Best Practice Quantifying Progress Multi-Dimensional Performance Profiling Conclusion - Recent Developments. Index.
From the reviews:"New Developments in Parsing Technology is a collection of papers based on contributions to the International workshop on Parsing Technology in the years 2000 and 2001. ... Collin's invited contribution is so outstanding that it alone makes it worthwhile to get hold of a copy of the book. Each of the selected workshop papers is a worthwhile read in itself ... ." (Stefan Riezler, Computational Linguistics, Vol. 32 (3), 2006)

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