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Grammatical Inference for Computational Linguistics (Paperback)
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Grammatical Inference for Computational Linguistics (Paperback)
Series: Synthesis Lectures on Human Language Technologies
Expected to ship within 10 - 15 working days
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This book provides a thorough introduction to the subfield of
theoretical computer science known as grammatical inference from a
computational linguistic perspective. Grammatical inference
provides principled methods for developing computationally sound
algorithms that learn structure from strings of symbols. The
relationship to computational linguistics is natural because many
research problems in computational linguistics are learning
problems on words, phrases, and sentences: What algorithm can take
as input some finite amount of data (for instance a corpus,
annotated or otherwise) and output a system that behaves
"correctly" on specific tasks? Throughout the text, the key
concepts of grammatical inference are interleaved with illustrative
examples drawn from problems in computational linguistics. Special
attention is paid to the notion of "learning bias." In the context
of computational linguistics, such bias can be thought to reflect
common (ideally universal) properties of natural languages. This
bias can be incorporated either by identifying a learnable class of
languages which contains the language to be learned or by using
particular strategies for optimizing parameter values. Examples are
drawn largely from two linguistic domains (phonology and syntax)
which span major regions of the Chomsky Hierarchy (from regular to
context-sensitive classes). The conclusion summarizes the major
lessons and open questions that grammatical inference brings to
computational linguistics. Table of Contents: List of Figures /
List of Tables / Preface / Studying Learning / Formal Learning /
Learning Regular Languages / Learning Non-Regular Languages /
Lessons Learned and Open Problems / Bibliography / Author
Biographies
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