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Titlebook: Information Retrieval and Natural Language Processing; A Graph Theory Appro Sheetal S. Sonawane,Parikshit N. Mahalle,Archana S Book 2022 Th

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书目名称Information Retrieval and Natural Language Processing
副标题A Graph Theory Appro
编辑Sheetal S. Sonawane,Parikshit N. Mahalle,Archana S
视频video
概述Provides a comprehensive view of graph theory in informational retrieval and natural language processing.Details understanding of graph theory basics, graph algorithms and networks using graph.Serves
丛书名称Studies in Big Data
图书封面Titlebook: Information Retrieval and Natural Language Processing; A Graph Theory Appro Sheetal S. Sonawane,Parikshit N. Mahalle,Archana S Book 2022 Th
描述.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..
出版日期Book 2022
关键词Graph Theory; Graph methods; Natural Language Processing; Information Retrieval; Data Science; Text Analy
版次1
doihttps://doi.org/10.1007/978-981-16-9995-5
isbn_softcover978-981-16-9997-9
isbn_ebook978-981-16-9995-5Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Text Document Pre-processing Using Graph Theoryre-processing algorithms are explained in the chapter with examples. The graph characteristics, properties, and operations useful to solve document preprocessing task is well explained in this chapter, The tools/libraries available are also provided in last section of the chapter.
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Text Analytics Using Graph Theoryhism is discussed in this chapter. The concept of term weighting in graph with generating extractive and abstractive summary by using graph operations is well explained with different graph types and properties. The applications like question answer system, sentiment analysis and recommendation syst
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