Colin de la Higuera – författare

The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.

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

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

This book constitutes the refereed proceedings of the Third International Colloquium on Grammatical Inference, ICGI-96, held in Montpellier, France, in September 1996.The 25 revised full papers contained in the book together with two invited key papers by Magerman and Knuutila were carefully selected for presentation at the conference. The papers are organized in sections on algebraic methods and algorithms, natural language and pattern recognition, inference and stochastic models, incremental methods and inductive logic programming, and operational issues.

This book facilitates understanding of how artificial intelligence (AI) aids and integrates digital transformation (DT) in education institutions worldwide in various scenarios: learning environments (learning innovation, learning management systems, data and analytics), emerging education trends (business trends, strategic technologies), administrative systems (recruit, retain, advance, enterprise business capabilities, student information systems), and digital strategy execution (business models and opportunities, strategic planning and governance). It serves as a reference for university lecturers, schoolteachers, policymakers, and international organizations, who will find in its various chapters practical recommendations and discoveries from practice, ready to be implemented in their contexts.Chapters 1, 2, 7 and 9 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.