Text, Speech and Language Technology

By: H. Bunt (Editor), John Carroll (Editor), Giorgio Satta (Editor)

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Paperback | 15 February 2005

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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 of the field for graduate students, and as a reference for established researchers.

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"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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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Natural Language & Machine Translation Language & Linguistics Linguistics Mathematics Applied Mathematics Computer Programming & Software Development Algorithms & Data Structures Audio Processing

Preface
Developments in Parsing Technology: From Theory to Application
Introduction
About this book
Parameter Estimation for Statistical Parsing Models: Theory and Practice of Distribution-Free Methods
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
High Precision Extraction of Grammatical Relations
Introduction
The Analysis System
Empirical Results
Conclusions and Further Work
Automated Extraction of TAGs from the Penn Treebank
Introduction
Tree Extraction Procedure
Evaluation
Extended Extracted Grammars
Related Work
Conclusions
Computing the Most Probable Parse for a Discontinuous Phrase-Structure Grammar
Introduction
Discontinuous Phrase-Structure Grammar
The Parsing Algorithm
Computing the Most Probable Parse
Experiments
Conclusion and Future Work
A Neural Network Parser that Handles Sparse Data
Introduction
Simple Synchrony Networks
A Probabilistic Parser for SSNs
Estimating the Probabilities with a Simple Synchrony Network
Generalizing from Sparse Data
Conclusion
An Efficient LR Parser Generator for Tree-Adjoining Grammars
TAGS
On Some Degenerate LR Models for TAGS
Proposed Algorithm
Implementation
Example
Some Properties Of the Algorithms
Evaluation
Conclusions
Relating Tabular Parsing Algorithms for LIG and TAG
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
Improved Left-Corner Chart Parsing for Large Context-Free Grammars
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
On Two Classes of Feature Paths in Large-Scale Unification Grammars
Introduction
Compiling the Quick Check Filter
Generalised Rule Reduction
Conclusion
A Context-Free Superset Approximation of Unification-Based Grammars
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
A Recognizer for Minimalist Languages
Introduction
Minimalist Grammars
Specification of the Recognizer
Correctness
Complexity Results
Conclusions and Future Work
Range Concatenation Grammars
Introduction
Positive Range Concatenation Grammars
Negative Range Concatenation Grammars
A Parsing Algorithm for RCGs
Closure Properties and Modularity
Conclusion
Grammar Induction by MDL-Based Distributional Classification
Introduction
Grammar Induction with the MDL Principle
Induction Strategies
MDL Induction by Dynamic Distributional Classification (DCC)
Comparison and Conclusion
Appendix
Optimal Ambiguity Packing in Context-Free Parsers with Interleaved Unification
Introduction
Ambiguity Packing in Context Free Parsing
The Rule Prioritization Heuristic
Empirical Evaluati
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Text, Speech and Language Technology

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Paperback

Industry Reviews

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