Foundations of Statistical Natural Language ProcessingStatistical 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. 
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Review: Foundations of Statistical Natural Language Processing
User Review  Rachid El guerrab  GoodreadsNeeds more walkthrough integrated examples, not just simple illustrations for specific paragraphs. It could also benefit from a discussion of NLP software and possible architectures for the domain. Read full review
Review: Foundations of Statistical Natural Language Processing
User Review  Michael Shaw  GoodreadsA must read for anyone looking to get into NLP. Teaches from first principles, including briefly touching on information theory/entropy. I felt it was well grounded, and proceded at a good pace. No ... Read full review
Contents
Introduction  3 
Mathematical Foundations  39 
Linguistic Essentials  81 
CorpusBased Work  117 
Collocations  151 
n gram Models over Sparse Data  191 
Word Sense Disambiguation  229 
Lexical Acquisition  265 
Probabilistic Context Free Grammars  381 
Probabilistic Parsing  407 
Statistical Alignment and Machine Translation  463 
Clustering  495 
Topics in Information Retrieval  529 
Text Categorization  575 
Tiny Statistical Tables  609 
657  