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020 _a9783030876951
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024 7 _a10.1007/978-3-030-87695-1
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
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082 0 4 _a006.312
_223
100 1 _aBanchs, Rafael E.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aText Mining with MATLAB®
_h[electronic resource] /
_cby Rafael E. Banchs.
250 _a2nd ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXII, 475 p. 86 illus., 85 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. 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 _aText 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 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aComputer software.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aMathematical Software.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030876944
776 0 8 _iPrinted edition:
_z9783030876968
856 4 0 _uhttps://doi.org/10.1007/978-3-030-87695-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178201
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