國立虎尾科技大學 |

Text Mining with MATLAB®

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Text Mining with MATLAB®/ by Rafael E. Banchs.
Author:	Banchs, Rafael E.
Description:	XII, 475 p. 86 illus., 85 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-87695-1
ISBN:	9783030876951

Text Mining with MATLAB®
Banchs, Rafael E.

Text Mining with MATLAB®[electronic resource] /by Rafael E. Banchs. - 2nd ed. 2021. - XII, 475 p. 86 illus., 85 illus. in color.online resource.

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

Standard No.: 10.1007/978-3-030-87695-1doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Text Mining with MATLAB®
LDR:03308nam a22003975i 4500 001 1056497
003 DE-He213
005 20211021200334.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030876951 $9 978-3-030-87695-1
024 7 $a 10.1007/978-3-030-87695-1 $2 doi
035 $a 978-3-030-87695-1
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Banchs, Rafael E. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 981871
245 1 0 $a Text Mining with MATLAB® $h [electronic resource] / $c by Rafael E. Banchs.
250 $a 2nd ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XII, 475 p. 86 illus., 85 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 0 $a Data mining. $3 528622
650 0 $a Information storage and retrieval. $3 1069252
650 0 $a Computer software. $3 528062
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Mathematical Software. $3 672446
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030876944
776 0 8 $i Printed edition: $z 9783030876968
856 4 0 $u https://doi.org/10.1007/978-3-030-87695-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)