About this book
The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions.
This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing.
Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. This book is intended for students and researchers interested in statistical approaches to Natural Language Processing.
Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach.
Eugene Charniak (Brown University)
- DOI https://doi.org/10.1007/978-3-642-22743-1
- Copyright Information Springer-Verlag Berlin Heidelberg 2012
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Humanities, Social Sciences and Law
- Print ISBN 978-3-642-22742-4
- Online ISBN 978-3-642-22743-1
- Series Print ISSN 2192-032X
- Series Online ISSN 2192-0338
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