EAN: 9783031010316

SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGI...

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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