Foundations of Statistical Natural Language Processing

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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.

Foundations of Statistical Natural Language Processing Review

This is the best book I've ever read on computational linguistics. It should be ideal for both linguists who want to learn about statistical language processing and those building language applications who want to learn about linguistics. This book isn't even published and it's now my most highly used reference book, joining gems such as Cormen, Leiserson and Rivest's algorithm book, Quirk et al.'s English Grammar, and Andrew Gelman's Bayesian statistics book (three excellent companions to this book, by the way).The book is written more like a computer science or math book in that it starts absolutely from scratch, but moves quickly and assumes a sophisticated reader. The first one hundred or so pages provide background in probability, information theory and linguistics. This book covers (almost) every current trend in NLP from a statistical perspective: syntactic tagging, sense disambiguation, parsing, information retrieval, lexical subcategorization, Hidden Markov Models, and probabilistic context-free grammars. It also covers machine translation and information retrieval in later chapters. It covers all the statistical techniques used in NLP from Bayes' law through to maximum entropy modeling, clustering: nearest neighbors and decision trees, and much more. What you won't find is information on applications to higher-level discourse and dialogue phenomena like pronoun resolution or speech act classification. Help other customers find the most helpful reviews� Was this review helpful to you? Yes No Report abuse | PermalinkComment�Comment

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