Foundations of Statistical Natural Language Processing
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.
Why Read This Book
You should read this book if you need a rigorous, algorithmic grounding in probabilistic models used for language tasks — n-gram models, smoothing, HMMs, EM, and probabilistic parsing — presented with clear math and worked examples. It gives you the conceptual tools and algorithms to implement language-modeling and tagging systems from scratch and to understand their statistical foundations.
Who Will Benefit
Engineers or researchers with some background in probability and programming who are building or evaluating language models, taggers, or probabilistic parsers (including those applying such models to speech recognition or language-aware communication systems).
Level: Intermediate — Prerequisites: Basic probability and statistics (discrete distributions, conditional probability), familiarity with algorithms and data structures, and comfort with mathematical notation; programming experience is helpful for implementations.
Key Takeaways
- Implement n-gram language models and evaluate them using perplexity.
- Apply smoothing and back-off techniques to sparse-language-count problems.
- Use the EM algorithm and Hidden Markov Models for sequence labeling and unsupervised learning.
- Build probabilistic parsers (PCFGs) and apply algorithms such as CKY for parsing.
- Perform lexical tasks like collocation finding and word-sense disambiguation using statistical methods.
- Measure and evaluate NLP systems with formal performance metrics (precision/recall, F-measure, perplexity).
Topics Covered
- 1. Introduction to Statistical NLP
- 2. Probability Theory and Information
- 3. N-gram Language Models
- 4. Smoothing and Back-off Methods
- 5. Part-of-Speech Tagging and Sequence Models
- 6. Hidden Markov Models and the EM Algorithm
- 7. Morphology and Word Knowledge
- 8. Collocations and Information Extraction
- 9. Probabilistic Parsing and PCFGs
- 10. Parsing Algorithms (CKY, Viterbi) and Evaluation
- 11. Word Sense Disambiguation and Lexical Semantics
- 12. Text Classification and Information Retrieval
- 13. Experimental Methodology and Evaluation Metrics
How It Compares
Covers similar foundational ground to Jurafsky & Martin's Speech and Language Processing but is more narrowly focused on statistical methods and algorithms; Jurafsky & Martin is broader and more recent with additional speech-specific content.
