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.
Note that Å·±¦ÓéÀÖ has erroneously catalogued the electronic and hardcover editions as separate books; you probably want to see , which has more reviews. A copy of my review follows.
A classic on natural language processing. If you know nothing about natural language processing, or have a piecemeal understanding, this book will give you an overview of the field in a rigorous and yet comprehensible way.
Note that this book was written in 1999, so it far predates the current practice to use recursive neural networks for natural language. This book will give you exactly what it says in the title, Foundations, not “modern best practices.�
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One of the few books I'd recommend to people getting started with NLP. However keep in mind that this is not an entirely beginner-oriented book. You need to have the basics of statistics and some programming to get along.