Foundations of Statistical Natural Language Processing

Overview

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Table of Contents

  1. List of Tables
  2. List of Figures
  3. Table of Notations
  4. Preface
  5. Road Map
  6. 1. Part I. Preliminaries
  7. 1. Introduction
  8. 2. Mathematical Foundations
  9. 3. Linguistics Essentials
  10. 4. Corpus-Based Work
  11. 2. Part II. Words
  12. 5. Collocations
  13. 6. Statistical Inference: n-gram Models over Sparse Data
  14. 7. Word Sense Disambiguation
  15. 8. Lexical Acquisition
  16. 3. Part III. Grammar
  17. 9. Markov Models
  18. 10. Part-of-Speech Tagging
  19. 11. Probabilistic Context Free Grammars
  20. 12. Probabilistic Parsing
  21. 4. Part IV. Applications and Techniques
  22. 13. Statistical Alignment and Machine Translation
  23. 14. Clustering
  24. 15. Topics in Information Retrieval
  25. 16. Text Categorization
  26. Tiny Statistical Tables
  27. Bibliography
  28. Index