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New Developments in Parsing Technology
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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.
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.
|Publisher||Springer-Verlag New York Inc.|
|Published in||United States|
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
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
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. Diaz, 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
1. Introduction. 2. Minimalist Grammars. 3. Specification of the Recognizer. 4. Correctness. 5. Complexity Results. 6. Conclusions and Future Work.
13: Range Concatenation Grammars
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 Rose.
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
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
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.