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Information retrieval and natural la...

Sonawane, Sheetal S.

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Information retrieval and natural language processing = a graph theory approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information retrieval and natural language processing/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Information retrieval - Mathematics. -
Online resource:	https://doi.org/10.1007/978-981-16-9995-5
ISBN:	9789811699955

Information retrieval and natural language processing = a 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. 104..

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

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

3596391
Information retrieval
--Mathematics.

LC Class. No.: ZA3075 / .S65 2022

Dewey Class. No.: 025.04

Information retrieval and natural language processing = a graph theory approach /
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245 1 0 $a Information retrieval and natural language processing $h [electronic resource] : $b a graph theory approach / $c by Sheetal S. Sonawane, Parikshit N. Mahalle, Archana S. Ghotkar.
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505 0 $a 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.
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.
650 0 $a Information retrieval $x Mathematics. $3 3596391
650 0 $a Natural language processing (Computer science) $x Mathematics. $3 3596392
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650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
700 1 $a Mahalle, Parikshit N. $3 3379506
700 1 $a Ghotkar, Archana S. $3 3596389
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