Grammatical Inference for Computational Linguistics - Heinz, Jeffrey

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Résumé :
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

Biographie:
Jeffrey Heinz received his Ph.D. from the University of California, Los Angeles in 2007, and is currently an Associate Professor at the University of Delaware. His research lies at the intersection of theoretical and mathematical linguistics, theoretical computer science, and computational learning theory, with specializations in phonology, linguistic typology, and grammatical inference. His work in these areas has appeared in the journals Linguistic Inquiry, Phonology, Theoretical Computer Science, Topics in Cognitive Science, Transactions of the Association of Computational Linguistics, and Science, among others. His current research interests are on establishing language-theoretic, automata-theoretic, model-theoretic, and logical characterizations of subregular classes of formal languages and transductions in order to better characterize the computational nature of phonological grammars and to better understand how they can be learned. He has served as part of the executive committee of the Association for Computational Linguistics Special Interest Group in Computational Morphology and Phonology (ACL-SIGMORPHON) since 2007. He has been a member of the steering committee of the International Community in Grammatical Inference (ICGI) since 2012. Moving forward, he would like to also support and strengthen the work of the Association for Mathematics of Language (MOL) and the Association for Logic, Language, and Information (FoLLI).