Information Retrieval and Natural Language Processing: A Graph Theory Approach

This book provides a comprehensive view of graph theory in informational retrieval (IR) and natural language processing (NLP), with examples and a strong foundation for beginners. It is divided into three parts and contains nine chapters.

Format: Paperback / softback
Length: 176 pages
Publication date: 24 February 2023
Publisher: Springer Verlag, Singapore

This comprehensive text delves into the realm of graph theory in informational retrieval (IR) and natural language processing (NLP), offering a thorough exploration of its applications. It presents a multitude of graph techniques tailored for IR and NLP purposes, accompanied by illustrative examples. Furthermore, the book serves as an invaluable resource for gaining a foundational understanding of graph theory concepts, algorithms, and networks. Structured into three parts, the book encompasses nine chapters, covering the fundamentals of graph theory, graph networks, and information retrieval with graph-based approaches. The second part delves into the basics of IR, emphasizing graph-based information retrieval techniques. The third part explores the recent and emerging applications of IR and NLP, supported by case studies utilizing graph theory.

What sets this book apart is its ability to provide a solid foundation for beginners seeking to leverage the power of mathematical structures, specifically graphs, in IR and NLP applications. It offers a clear and organized presentation of technical details, encompassing the tools and technologies employed for graph algorithms and their implementation in these fields. Additionally, the book provides insights into the future scope of graph algorithms and their potential applications, making it an invaluable resource for both practitioners and scholars in the domain.

Weight: 308g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811699979
Edition number: 1st ed. 2022