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New Developments in Parsing Technology
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New Developments in Parsing Technology
Paperback ISBN: 9781402022944
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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. This book contains contributions from researchers in the area of natural language parsing technology. It is suitable for graduate students and researchers.
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.
ISBN  1402022948 
ISBN13  9781402022944 
Publisher  SpringerVerlag New York Inc. 
Format  Paperback 
Publication date  15/02/2005 
Pages  405 
Weight (grammes)  586 
Published in  United States 
Height (mm)  235 
Width (mm)  155 
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 DistributionFree Methods
M. Collins.
1. Introduction. 2. Linear Models. 3. Probabilistic ContextFree 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 PhraseStructure Grammar
O. Plaehn. 1. Introduction. 2. Discontinuous PhraseStructure 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 TreeAdjoining 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. Diaz, M. Vilares.
1. Introduction. 2. TreeAdjoining Grammars. 3. Linear Indexed Grammars. 4. BottomupParsing Algorithms. 5. Barleylike Parsing Algorithms. 6. Barleylike Parsing Algorithms Preserving the Correct Prefix Property. 7. Bidirectional Parsing. 8. Specialized TAG parsers. 9. Conclusion.
9: Improved LeftCorner Chart Parsing for Large ContextFree Grammars
R.C. Moore.
1. Introduction. 2. Evaluating Parsing Algorithms. 3. Terminology and Notation. 4. Test Grammars. 5. LeftCorner 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 LargeScale Unification Grammars
L. Ciortuz. 1. Introduction. 2. Compiling the Quick Check Filter. 3. Generalised Rule Reduction. 4. Conclusion.
11: A ContextFree Superset Approximation of UnificationBased 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 MDLBased 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 ContextFree Parsers with Interleaved Unification
A. Lavie, C. Penstein Rose.
1.