Humanist Discussion Group, Vol. 17, No. 827.
Centre for Computing in the Humanities, King's College London
www.kcl.ac.uk/humanities/cch/humanist/
www.princeton.edu/humanist/
Submit to: humanist@princeton.edu
Date: Tue, 27 Apr 2004 07:10:27 +0100
From: Kluwer <Kluwer@kluwer.m0.net>
Subject: new book: New Developments in Parsing Technology
New Developments in Parsing Technology
edited by
Harry Bunt
Tilburg University, The Netherlands
John Carroll
University of Sussex, Brighton, UK
Giorgio Satta
University of Padua, Italy
TEXT, SPEECH AND LANGUAGE TECHNOLOGY -- 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.
CONTENTS AND CONTRIBUTORS
* Preface.
* 1: Developments in Parsing Technology: From Theory to Application; H.
Bunt, J. Carroll, G. Satta. 1. Introduction. 2. About this book.
* 2: Parameter Estimation for Statistical Parsing Models: Theory and
Practice of Distribution-Free Methods; M. Collins. 1. Introduction. 2.
Linear Models. 3. Probabilistic Context-Free Grammars. 4. Statistical
Learning Theory. 5. Convergence Bounds for Finite Sets of Hypotheses. 6.
Convergence Bounds for Hyperplane Classifiers. 7. Application of Margin
Analysis to Parsing. 8. Algorithms. 9. Discussion. 10. Conclusions.
* 3: High Precision Extraction of Grammatical Relations; J. Carroll, T.
Briscoe. 1. Introduction. 2. The Analysis System. 3. Empirical Results. 4.
Conclusions and Further Work.
* 4: Automated Extraction of TAGs from the Penn Treebank; J. Chen, K.V.
Shanker. 1. Introduction. 2. Tree Extraction Procedure. 3. Evaluation. 4.
Extended Extracted Grammars. 5. Related Work. 6. Conclusions.
* 5: Computing the Most Probable Parse for a Discontinuous
Phrase-Structure Grammar; O. Plaehn. 1. Introduction. 2. Discontinuous
Phrase-Structure Grammar. 3. The Parsing Algorithm. 4. Computing the Most
Probable Parse. 5. Experiments. 6. Conclusion and Future Work.
* 6: A Neural Network Parser that Handles Sparse Data; J. Henderson. 1.
Introduction. 2. Simple Synchrony Networks. 3. A Probabilistic Parser for
SSNs. 4. Estimating the Probabilities with a Simple Synchrony Network. 5.
Generalizing from Sparse Data. 6. Conclusion.
* 7: An Efficient LR Parser Generator for Tree-Adjoining Grammars; C.A.
Prolo. 1. Introduction. 2. TAGS. 3. On Some Degenerate LR Models for TAGS.
4. Proposed Algorithm. 5. Implementation. 6. Example. 7. Some Properties Of
the Algorithms. 8. Evaluation. 9. Conclusions.
* 8: Relating Tabular Parsing Algorithms for LIG and TAG; M.A. Alonso,
E. de la Clergerie, V.J. DÃaz, M. Vilares. 1. Introduction. 2.
Tree-Adjoining Grammars. 3. Linear Indexed Grammars. 4. Bottom-up Parsing
Algorithms. 5. Barley-like Parsing Algorithms. 6. Barley-like Parsing
Algorithms Preserving the Correct Prefix Property. 7. Bidirectional
Parsing. 8. Specialized TAG parsers. 9. Conclusion.
* 9: Improved Left-Corner Chart Parsing for Large Context-Free
Grammars; R.C. Moore. 1. Introduction. 2. Evaluating Parsing Algorithms. 3.
Terminology and Notation. 4. Test Grammars. 5. Left-Corner Parsing
Algorithms and Refinements. 6. Grammar Transformations. 7. Extracting
Parses from the Chart. 8. Comparison to Other Algorithms. 9. Conclusions.
* 10: On Two Classes of Feature Paths in Large-Scale Unification
Grammars; L. Ciortuz. 1. Introduction. 2. Compiling the Quick Check Filter.
3. Generalised Rule Reduction. 4. Conclusion.
* 11: A Context-Free Superset Approximation of Unification-Based
Grammars; B. Kiefer, H.-U. Krieger. 1. Introduction. 2. Basic Inventory. 3.
Approximation as Fixpoint Construction. 4. The Basic Algorithm. 5.
Implementation Issues and Optimizations. 6. Revisiting the Fixpoint
Construction. 7. Three Grammars. 8. Disambiguation of UBGs via
Probabilistic Approximations.
* 12: A Recognizer for Minimalist Languages; H. Harkema. 1.
Introduction. 2. Minimalist Grammars. 3. Specification of the Recognizer.
4. Correctness. 5. Complexity Results. 6. Conclusions and Future Work.
* 13: Range Concatenation Grammars; P. Boullier. 1. Introduction. 2.
Positive Range Concatenation Grammars. 3. Negative Range Concatenation
Grammars. 4. A Parsing Algorithm for RCGs. 5. Closure Properties and
Modularity. 6. Conclusion.
* 14: Grammar Induction by MDL-Based Distributional Classification;
Yikun Guo, Fuliang Weng, Lide Wu. 1. Introduction. 2. Grammar Induction
with the MDL Principle. 3. Induction Strategies. 4. MDL Induction by
Dynamic Distributional Classification (DCC). 5. Comparison and Conclusion.
Appendix.
* 15: Optimal Ambiguity Packing in Context-Free Parsers with
Interleaved Unification; A. Lavie, C. Penstein Rosé. 1. Introduction. 2.
Ambiguity Packing in Context Free Parsing. 3. The Rule Prioritization
Heuristic. 4. Empirical Evaluations and Discussion. 5. Conclusions and
Future Directions.
* 16: Robust Data-Oriented Spoken Language Understanding; K. Sima'an.
1. Introduction. 2. Brief Overview of OVIS. 3. OP vs. Tree-Gram. 4.
Application to the OVIS Domain. 5. Conclusions.
* 17: SOUP: A Parser for Real-World Spontaneous Speech; M. Gavaldà . 1.
Introduction. 2. Grammar Representation. 3. Sketch of the Parsing
Algorithm. 4. Performance. 5. Key Features. 6. Conclusion.
* 18: Parsing and Hypergraphs; D. Klein, C.D. Manning. 1. Introduction.
2. Hypergraphs and Parsing. 3. Viterbi Parsing Algorithm. 4. Analysis. 5.
Conclusion. Appendix.
* 19: Measure for Measure: Towards Increased Component Comparability
and Exchange; S. Oepen, U. Callmeier. 1. Competence & Performance
Profiling. 2. Strong Empiricism: A Few Examples. 3. PET - Synthesizing
Current Best Practice. 4. Quantifying Progress. 5. Multi-Dimensional
Performance Profiling. 6. Conclusion - Recent Developments.
* Index.
Hardbound ISBN: 1-4020-2293-X Date: June 2004 Pages: 216 pp.
EUR 139.00 / USD 153.00 / GBP 96.00
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