Memory-Based Parsing

Memory-Based Parsing

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Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks. This monograph describes the application of MBL to robust parsing. Robust parsing using MBL can provide added functionality for key NLP applications, such as Information Retrieval, Information Extraction, and Question Answering, by facilitating more complex syntactic analysis than is currently available. The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing. Since parsing is not easily characterizable as a classification task, adaptations of standard MBL are necessary. These adaptations can either take the form of a cascade of local classifiers or of a holistic approach for selecting a complete tree.The text provides excellent course material on MBL. It is equally relevant for any researcher concerned with symbolic machine learning, Information Retrieval, Information Extraction, and Question Answering.
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Product details

  • Hardback | 294 pages
  • 149.9 x 226.1 x 22.9mm | 498.96g
  • John Benjamins Publishing Co
  • Amsterdam, Netherlands
  • English
  • 1588115909
  • 9781588115904

Table of contents

1. 1. Introduction; 2. 2. Memory-Based Learning; 3. 3. Memory-Based Approaches to Parsing; 4. 4. Data-Oriented Parsing; 5. 5. TuSBL: A Memory-Based Parser; 6. 6. Empirical Evaluation; 7. 7. A Comparison of Memory-Based Approaches to TuSBL; 8. 8. Conclusion and Future Directions; 9. Appendix A. The Stuttgart-Tubingen Tagset; 10. Appendix B The TuBa-D/S Inventory of Syntactic Categories and Grammatical Functions; 11. References; 12. Index of Subjects and Terms
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