Название: Text Mining with MATLAB, Second EditionАвтор: Rafael E. Banchs
Издательство: Springer
Год: 2021
Страниц: 472
Язык: английский
Формат: pdf (true)
Размер: 25.3 MB
Text Mining with MATLAB provides a comprehensive introduction to text mining using MATLAB. It is designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications.
In my experience the MATLAB programming platform has always been an excellent tool for conducting experimental research and proof of concepts, as well as for implementing prototypes and applications. However, in the natural language processing (NLP) community, with the exception of a few Machine Learning practitioners that have entered in the community via text mining applications, there is not a well-established culture of using the MATLAB platform. As a technical computing software that specializes in operating with matrices and vectors, MATLAB offers and excellent framework for text mining and natural language processing research and development.
This book has been written with two objectives in mind. It aims at opening the doors of natural language research and applications to MATLAB users from other disciplines, as well as introducing the new practitioners in the field to the many possibilities offered by the MATLAB programming platform. The book has been conceived as an introductory book, which should be easy to follow and digest. All examples and figures presented in the book can be reproduced by following the same procedures described for each case.
The book is structured in three main parts: The first part, Fundamentals, introduces basic procedures and methods for manipulating and operating with text within the MATLAB programming environment. The second part of the book, Mathematical Models, is devoted to motivating, introducing, and explaining the two main paradigms of mathematical models most commonly used for representing text dаta: the statistical and the geometrical approach. Eventually, the third part of the book, Techniques and Applications, addresses general problems in text mining and natural language processing applications such as document categorization, document search, content analysis, summarization, question answering, and conversational systems. This second edition includes updates in line with the recently released “Text Analytics Toolbox” within the MATLAB product and introduces three new chapters and six new sections in existing ones.
All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.
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