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Author: Erin Suzan Isbilen Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Understanding the computations involved in language acquisition is a central topic in cognitive science. This dissertation presents four empirical papers that investigate the role of domain general cognitive processes in the learning of linguistic structure. The first paper describes the contribution of chunking-a basic memory process-to the phenomenon known as statistical learning, which describes learners' ability to leverage the regularities present in the environment to form concrete representations of the input, such as finding the words in speech. The second paper extends these findings by showing how chunking can also account for the statistical learning and generalization of non-adjacent dependencies, a key feature of many linguistic systems. The third paper demonstrates that individual differences in statistically-based chunking of artificial language statistics significantly predicts sensitivity to comparable statistical structures in natural language. The final paper presents a meta-analysis of nearly 500 peer-reviewed studies on statistical learning in infants, children, and adults, tests its utility across different language properties, and proposes several methodological considerations that may benefit future experimentation. Together, these studies highlight the fundamental contribution of basic, domain general computations to language-and how they may even shape the evolution of linguistic structure over time.
Author: Erin Suzan Isbilen Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Understanding the computations involved in language acquisition is a central topic in cognitive science. This dissertation presents four empirical papers that investigate the role of domain general cognitive processes in the learning of linguistic structure. The first paper describes the contribution of chunking-a basic memory process-to the phenomenon known as statistical learning, which describes learners' ability to leverage the regularities present in the environment to form concrete representations of the input, such as finding the words in speech. The second paper extends these findings by showing how chunking can also account for the statistical learning and generalization of non-adjacent dependencies, a key feature of many linguistic systems. The third paper demonstrates that individual differences in statistically-based chunking of artificial language statistics significantly predicts sensitivity to comparable statistical structures in natural language. The final paper presents a meta-analysis of nearly 500 peer-reviewed studies on statistical learning in infants, children, and adults, tests its utility across different language properties, and proposes several methodological considerations that may benefit future experimentation. Together, these studies highlight the fundamental contribution of basic, domain general computations to language-and how they may even shape the evolution of linguistic structure over time.
Author: Patrick Rebuschat Publisher: Walter de Gruyter ISBN: 1934078247 Category : Psychology Languages : en Pages : 524
Book Description
Open publication This volume brings together contributors from cognitive psychology, theoretical and applied linguistics, as well as computer science, in order to assess the progress made in statistical learning research and to determine future directions. An important objective is to critically examine the role of statistical learning in language acquisition. While most contributors agree that statistical learning plays a central role in language acquisition, they have differing views. This book will promote the development of the field by fostering discussion and collaborations across disciplinary boundaries.
Author: Gloria Corpas Pastor Publisher: Springer Nature ISBN: 3030301354 Category : Computers Languages : en Pages : 445
Book Description
This book constitutes the refereed proceedings of the Third International Conference on Computational and Corpus-Based Phraseology, Europhras 2019, held in Malaga, Spain, in September 2019. The 31 full papers presented in this book were carefully reviewed and selected from 116 submissions. The papers in this volume cover a number of topics including general corpus-based approaches to phraseology, phraseology in translation and cross-linguistic studies, phraseology in language teaching and learning, phraseology in specialized languages, phraseology in lexicography, cognitive approaches to phraseology, the computational treatment of multiword expressions, and the development, annotation, and exploitation of corpora for phraseological studies.
Author: Vladimir Vapnik Publisher: Springer Science & Business Media ISBN: 1475732643 Category : Mathematics Languages : en Pages : 324
Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Author: S. Armstrong Publisher: Springer Science & Business Media ISBN: 9401723907 Category : Language Arts & Disciplines Languages : en Pages : 314
Book Description
ABOUT THIS BOOK This book is intended for researchers who want to keep abreast of cur rent developments in corpus-based natural language processing. It is not meant as an introduction to this field; for readers who need one, several entry-level texts are available, including those of (Church and Mercer, 1993; Charniak, 1993; Jelinek, 1997). This book captures the essence of a series of highly successful work shops held in the last few years. The response in 1993 to the initial Workshop on Very Large Corpora (Columbus, Ohio) was so enthusias tic that we were encouraged to make it an annual event. The following year, we staged the Second Workshop on Very Large Corpora in Ky oto. As a way of managing these annual workshops, we then decided to register a special interest group called SIGDAT with the Association for Computational Linguistics. The demand for international forums on corpus-based NLP has been expanding so rapidly that in 1995 SIGDAT was led to organize not only the Third Workshop on Very Large Corpora (Cambridge, Mass. ) but also a complementary workshop entitled From Texts to Tags (Dublin). Obviously, the success of these workshops was in some measure a re flection of the growing popularity of corpus-based methods in the NLP community. But first and foremost, it was due to the fact that the work shops attracted so many high-quality papers.
Author: Ke-Lin Du Publisher: Springer Nature ISBN: 1447174526 Category : Mathematics Languages : en Pages : 988
Book Description
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Author: Jude W. Shavlik Publisher: Morgan Kaufmann ISBN: 9781558601437 Category : Computers Languages : en Pages : 868
Book Description
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.
Author: Tom M. Mitchell Publisher: Springer Science & Business Media ISBN: 1461322790 Category : Computers Languages : en Pages : 413
Book Description
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.
Author: Erin Suzan Isbilen
Author: Erin Suzan Isbilen
Author: Patrick Rebuschat
Author: Gloria Corpas Pastor
Author: Vladimir Vapnik
Author: S. Armstrong
Author: Ryszard Stanisław Michalski
Author: Ke-Lin Du
Author: Jude W. Shavlik
Author: Tom M. Mitchell
Author: Marianne Hundt