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Text mining with MATLAB

Banchs, Rafael E.

Text mining with MATLAB

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Text mining with MATLABby Rafael E. Banchs.
作者:	Banchs, Rafael E.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xii, 475 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Text data mining.
電子資源:	https://doi.org/10.1007/978-3-030-87695-1
ISBN:	9783030876951$q(electronic bk.)

Text mining with MATLAB
Banchs, Rafael E.

Text mining with MATLAB[electronic resource] /by Rafael E. Banchs. - Second edition. - Cham :Springer International Publishing :2021. - xii, 475 p. :ill. (some col.), digital ;24 cm.

1. Introduction -- PART I: FUNDAMENTALS -- 2. Handling Text Data -- 3. Regular Expressions -- 4. Basic Operations with Strings -- 5. Reading and Writing Files -- 6. The Structure of Language -- PART II: MATHEMATICAL MODELS -- 7. Basic Corpus Statistics -- 8. Statistical Models -- 9. Geometrical Models -- 10. Dimensionality Reduction -- PART III: METHODS AND APPLICATIONS -- 11. Document Categorization -- 12. Document Search -- 13. Content Analysis -- 14. Keyword Extraction and Summarization -- 15. Question Answering and Dialogue.

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. 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 data: 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.

ISBN: 9783030876951$q(electronic bk.)

Standard No.: 10.1007/978-3-030-87695-1doiSubjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

892173
Text data mining.

LC Class. No.: QA76.9.D343 / B35 2021

Dewey Class. No.: 006.312

Text mining with MATLAB
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505 0 $a 1. Introduction -- PART I: FUNDAMENTALS -- 2. Handling Text Data -- 3. Regular Expressions -- 4. Basic Operations with Strings -- 5. Reading and Writing Files -- 6. The Structure of Language -- PART II: MATHEMATICAL MODELS -- 7. Basic Corpus Statistics -- 8. Statistical Models -- 9. Geometrical Models -- 10. Dimensionality Reduction -- PART III: METHODS AND APPLICATIONS -- 11. Document Categorization -- 12. Document Search -- 13. Content Analysis -- 14. Keyword Extraction and Summarization -- 15. Question Answering and Dialogue.
520 $a 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. 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 data: 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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