Jeffrey Heinz – författare

Stress and accent are central, organizing features of grammar, but their precise nature continues to be a source of mystery and wonder. These issues come to the forefront in acquisition, where the tension between the abstract mental representations and the concrete physical manifestations of stress and accent is deeply reflected. Understanding the nature of the representations of stress and accent patterns, and understanding how stress and accent patterns are learned, informs all aspects of linguistic theory and language acquisition. These two themes - representation and acquisition - form the organizational backbone of this book. Each is addressed along different dimensions of stress and accent, including the position of an accent or stress within various prosodic domains and the acoustic dimensions along which the pronunciation of stress and accent may vary. The research presented in the book is multidisciplinary, encompassing theoretical linguistics, speech science, and computational and experimental research.

Stress and accent are central, organizing features of grammar, but their precise nature continues to be a source of mystery and wonder. These issues come to the forefront in the phonetic manifestation of stress and accent, their cross-linguistic variation and the subtle and intricate laws they obey in individual languages. Understanding the nature of stress and accent systems informs all aspects of linguistic theory, methods, typology and especially the grammatical analysis of language data. These themes form the organizational backbone of this book. Bringing together a team of world-renowned phonologists, the volume covers a range of typological and theoretical issues in the study of stress and accent. It will appeal to researchers who value synergistic approaches to the study of stress and accent, careful attention to cross-linguistic variation, and detailed analyzes of both well-studied and understudied languages. The book is a lively testimony of a field of inquiry that shows progress, while also identifying questions for ongoing research.

Stress and accent are central, organizing features of grammar, but their precise nature continues to be a source of mystery and wonder. These issues come to the forefront in acquisition, where the tension between the abstract mental representations and the concrete physical manifestations of stress and accent is deeply reflected. Understanding the nature of the representations of stress and accent patterns, and understanding how stress and accent patterns are learned, informs all aspects of linguistic theory and language acquisition. These two themes - representation and acquisition - form the organizational backbone of this book. Each is addressed along different dimensions of stress and accent, including the position of an accent or stress within various prosodic domains and the acoustic dimensions along which the pronunciation of stress and accent may vary. The research presented in the book is multidisciplinary, encompassing theoretical linguistics, speech science, and computational and experimental research.

Stress and accent are central, organizing features of grammar, but their precise nature continues to be a source of mystery and wonder. These issues come to the forefront in the phonetic manifestation of stress and accent, their cross-linguistic variation and the subtle and intricate laws they obey in individual languages. Understanding the nature of stress and accent systems informs all aspects of linguistic theory, methods, typology and especially the grammatical analysis of language data. These themes form the organizational backbone of this book. Bringing together a team of world-renowned phonologists, the volume covers a range of typological and theoretical issues in the study of stress and accent. It will appeal to researchers who value synergistic approaches to the study of stress and accent, careful attention to cross-linguistic variation, and detailed analyzes of both well-studied and understudied languages. The book is a lively testimony of a field of inquiry that shows progress, while also identifying questions for ongoing research.

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 explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first three chapters ofthe book deal with issues regarding theoretical learning frameworks; the nextfour chapters focus on the main classes of formal languages according toChomsky's hierarchy, in particular regular and context-free languages; and thefinal chapter addresses the processing of biosequences. The topics chosen are of foundational interest withrelatively mature and established results, algorithms and conclusions. The bookwill be of value to researchers and graduate students in areas such astheoretical computer science, machine learning, computational linguistics, bioinformatics,and cognitive psychology who are engaged with the study of learning, especiallyof the structure underlying the concept to be learned. Some knowledge ofmathematics and theoretical computer science, including formal language theory,automata theory, formal grammars, and algorithmics, is a prerequisite forreading this book.