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001444665 001__ 1444665
001444665 003__ OCoLC
001444665 005__ 20230310003721.0
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001444665 008__ 220226s2022\\\\si\\\\\\ob\\\\000\0\eng\d
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001444665 020__ $$a9789811699955$$q(electronic bk.)
001444665 020__ $$a981169995X$$q(electronic bk.)
001444665 020__ $$z9811699941
001444665 020__ $$z9789811699948
001444665 0247_ $$a10.1007/978-981-16-9995-5$$2doi
001444665 035__ $$aSP(OCoLC)1299386655
001444665 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dN$T$$dSFB$$dOCLCQ$$dUKAHL$$dOCLCQ
001444665 049__ $$aISEA
001444665 050_4 $$aZA3075
001444665 08204 $$a025.04$$223
001444665 1001_ $$aSonawane, Sheetal S.
001444665 24510 $$aInformation retrieval and natural language processing :$$ba graph theory approach /$$cSheetal S. Sonawane, Parikshit N. Mahalle, Archana S. Ghotkar.
001444665 260__ $$aSingapore :$$bSpringer,$$c2022.
001444665 300__ $$a1 online resource (186 pages)
001444665 336__ $$atext$$btxt$$2rdacontent
001444665 337__ $$acomputer$$bc$$2rdamedia
001444665 338__ $$aonline resource$$bcr$$2rdacarrier
001444665 4901_ $$aStudies in big data ;$$vv. 104
001444665 504__ $$aIncludes bibliographical references.
001444665 5050_ $$aPart A -- Chapter 1. Graph theory basics -- Chapter 2. Graph Algorithms -- Chapter 3. Networks using graph -- Part B -- Chapter 4. Information retrieval -- Chapter 5. Text document preprocessing using graph theory -- Chapter 6. Text analytics using graph theory -- Chapter 7. Knowledge graph -- Part C -- Chapter 8. Emerging Applications and development -- Chapter 9. Conclusion and future scope.
001444665 506__ $$aAccess limited to authorized users.
001444665 520__ $$aThis book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.
001444665 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 8, 2022).
001444665 650_0 $$aInformation retrieval$$xMathematics.
001444665 650_0 $$aNatural language processing (Computer science)$$xMathematics.
001444665 650_0 $$aGraph theory.
001444665 650_6 $$aRecherche de l'information$$xMathématiques.
001444665 650_6 $$aTraitement automatique des langues naturelles$$xMathématiques.
001444665 655_7 $$aLlibres electrònics.$$2thub
001444665 655_0 $$aElectronic books.
001444665 7001_ $$aMahalle, Parikshit N.
001444665 7001_ $$aGhotkar, Archana S.
001444665 77608 $$iPrint version:$$aSonawane, Sheetal S.$$tInformation Retrieval and Natural Language Processing.$$dSingapore : Springer Singapore Pte. Limited, ©2022$$z9789811699948
001444665 830_0 $$aStudies in big data ;$$vv. 104.
001444665 852__ $$bebk
001444665 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-9995-5$$zOnline Access$$91397441.1
001444665 909CO $$ooai:library.usi.edu:1444665$$pGLOBAL_SET
001444665 980__ $$aBIB
001444665 980__ $$aEBOOK
001444665 982__ $$aEbook
001444665 983__ $$aOnline
001444665 994__ $$a92$$bISE