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Information retrieval and natural language processinga graph theory approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information retrieval and natural language processingby Sheetal S. Sonawane, Parikshit N. Mahalle, Archana S. Ghotkar.
Reminder of title:	a graph theory approach /
Author:	Sonawane, Sheetal S.
other author:	Mahalle, Parikshit N.
Published:	Singapore :Springer Singapore :2022.
Description:	xix, 176 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Information retrievalMathematics.
Online resource:	https://doi.org/10.1007/978-981-16-9995-5
ISBN:	9789811699955$q(electronic bk.)

Information retrieval and natural language processinga graph theory approach /
Sonawane, Sheetal S.

Information retrieval and natural language processinga graph theory approach /[electronic resource] :by Sheetal S. Sonawane, Parikshit N. Mahalle, Archana S. Ghotkar. - Singapore :Springer Singapore :2022. - xix, 176 p. :ill., digital ;24 cm. - Studies in big data,v. 1042197-6511 ;. - Studies in big data ;v.1..

Part 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.

This 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.

ISBN: 9789811699955$q(electronic bk.)

Standard No.: 10.1007/978-981-16-9995-5doiSubjects--Topical Terms:

925279
Information retrieval
--Mathematics.

LC Class. No.: ZA3075 / .S65 2022

Dewey Class. No.: 025.04

Information retrieval and natural language processinga graph theory approach /
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520 $a This 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.
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