Foundations of Statistical Natural Language Processing

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MIT Press, 1999 - Language Arts & Disciplines - 680 pages
20 Reviews

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.

 

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User Review  - billlund - LibraryThing

Although 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
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
Index
657

Markov Models
317
PartofSpeech Tagging
341

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About the author (1999)

Christopher Manning is an Associate Professor of Computer Science and Linguistics at Stanford University. His research concentrates on probabilistic models of language and statistical natural language processing, information extraction, text understanding and text mining.

Dr Hinrich Schutze resides as Chair of Theoretical Computational Linguistics at the Institute for Natural Language Processing, University of Stuttgart.

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