17.827 new book on parsing

From: Humanist Discussion Group (by way of Willard McCarty willard.mccarty@kcl.ac.uk)
Date: Fri May 07 2004 - 16:50:03 EDT

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                   Humanist Discussion Group, Vol. 17, No. 827.
           Centre for Computing in the Humanities, King's College London
                         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


    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
    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. 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. Díaz, 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.
         * 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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