Artificial Companion for Second Language Conversation: Chatbots Support Practice Using Conversation Analysis, Hцhn Sviatlana
Автор: Neapolitan, Richard E. (northeastern Illinois University, Illinois, Usa) Jiang, Xia (university Of Pittsburgh, Pennsylvania, Usa) Название: Contemporary artificial intelligence, second edition ISBN: 1138502383 ISBN-13(EAN): 9781138502383 Издательство: Taylor&Francis Рейтинг: Цена: 122490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update retains the same accessibility and problem-solving approach, while providing new material and methods, including neural networks and deep learning.
Автор: Sviatlana H?hn Название: Artificial Companion for Second Language Conversation ISBN: 303015503X ISBN-13(EAN): 9783030155032 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The research described in this book shows that conversation analysis can effectively model dialogue. Specifically, this work shows that the multidisciplinary field of communicative ICALL may greatly benefit from including Conversation Analysis. As a consequence, this research makes several contributions to the related research disciplines, such as conversation analysis, second-language acquisition, computer-mediated communication, artificial intelligence, and dialogue systems.The book will be of value for researchers and engineers in the areas of computational linguistics, intelligent assistants, and conversational interfaces.
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language.
Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.
In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Автор: Rafael Magdalena-Benedito, Marcelino Martinez-Sober, Jose Maria Martinez-Martinez, Pablo Escandell-Moreno, Joan Vila-Frances Название: Intelligent Data Analysis for Real-Life Applications: Theory and Practice ISBN: 146661806X ISBN-13(EAN): 9781466618060 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 189420.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (IDA) to these data sets through the design and development of algorithms and techniques to extract knowledge from databases. This pivotal reference explores practical applications of IDA, and it is essential for academic and research libraries as well as students, researchers, and educators in data analysis, application development, and database management.
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language.
Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.
In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
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