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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LibraryThing Review
User Review  billlund  LibraryThingAlthough the required text for the class in statistical natural language processing, this was not as clear, particularly regarding algorithms, as "Speech and Language Processing" by Jurafsky and Martin. Read full review
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nice book
Contents
Introduction  3 
Mathematical Foundations  39 
Linguistic Essentials  81 
CorpusBased Work  117 
Collocations  151 
ngram 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 
620  
Other editions  View all
Foundations of Statistical Natural Language Processing Christopher Manning,Hinrich Schutze Limited preview  1999 
Foundations of Statistical Natural Language Processing Christopher Manning,Hinrich Schutze Limited preview  1999 
Common terms and phrases
actually algorithm alignment ambiguous applied approach assume attachment basic bigrams Brown calculate chapter classes clustering collocations compute context corpus corresponding counts defined depends derivation described determine dictionary disambiguation discussed distribution documents English entropy estimates example Exercise expected expression figure frequency function give given grammar head important independent indicates initial interest language learning lexical likelihood linguistics look Markov meaning measure methods ngram node normally Note noun object occur parameters parsing particular performance phrase position possible preposition present probabilistic probability problem question random refer rule selectional semantic sense sentence sequence shown shows similarity simply space speech Statistical Statistical NLP structure syntactic tagger theory things tion translation tree usually values variable vectors verb words