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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 RosÃ©. 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. GavaldÃ . 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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