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
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)