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＜電子ブック＞
Information Retrieval and Natural Language Processing : A Graph Theory Approach / by Sheetal S. Sonawane, Parikshit N. Mahalle, Archana S. Ghotkar
(Studies in Big Data. ISSN:21976511 ; 104)

版	1st ed. 2022.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2022
本文言語	英語
大きさ	XIX, 176 p. 171 illus., 118 illus. in color : online resource
著者標目	*Sonawane, Sheetal S author Mahalle, Parikshit N author Ghotkar, Archana S author SpringerLink (Online service)
件　名	LCSH:Graph theory LCSH:Data protection LCSH:Natural language processing (Computer science) LCSH:Machine learning LCSH:Artificial intelligence -- Data processing  全ての件名で検索 FREE:Graph Theory FREE:Data and Information Security FREE:Natural Language Processing (NLP) FREE:Machine Learning FREE:Data Science
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-981-16-9995-5

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