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Grammatical Inference for Computational Linguistics / by Jeffrey Heinz, Colin de la Higuera, Menno van Zaanen.

Springer Nature Synthesis Collection of Technology Collection 6 Available online

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Format:
Book
Author/Creator:
Heinz, Jeffrey., Author.
De la Higuera, Colin, Author.
Zaanen, Menno van., Author.
Series:
Synthesis Lectures on Human Language Technologies, 1947-4059
Language:
English
Subjects (All):
Artificial intelligence.
Natural language processing (Computer science).
Computational linguistics.
Artificial Intelligence.
Natural Language Processing (NLP).
Computational Linguistics.
Local Subjects:
Artificial Intelligence.
Natural Language Processing (NLP).
Computational Linguistics.
Physical Description:
1 online resource (XXI, 139 p.)
Edition:
1st ed. 2016.
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2016.
Summary:
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.
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.
ISBN:
9783031021596
3031021592

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