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. The 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)"
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