1st Edition

Natural Language Processing and Information Retrieval Principles and Applications

    270 Pages 40 Color & 8 B/W Illustrations
    by CRC Press

    270 Pages 40 Color & 8 B/W Illustrations
    by CRC Press

    This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.


    • Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation
    • Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data
    • Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining
    • Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing
    • Covers latest datasets for natural language processing and information retrieval for social media like Twitter

    The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.


    Chapter 1

    Federated Learning for Natural Language Processing

    Sergei Ternovykh, Anastasia Nikiforova


    Chapter 2

    Utility-Based Recommendation System For Large Datasets Using EAHUIM

    Vandna Dahiya


    Chapter 3

    Anaphora Resolution: A Complete View with Case Study

    Kalpana B. Khandale, C. Namrata Mahender


    Chapter 4

    A Review of the Approaches to Neural Machine Translation          

    Preetpal Kaur Buttar, Manoj Kumar Sachan


    Chapter 5

    Evolution of Question-Answering System from Information Retrieval: A Scientific Time Travel from Bangla

    Bangla Arijit Das, Diganta Saha


    Chapter 6

    Recent Advances in Textual Code Switching 

    Sergei Ternovykh, Anastasia Nikiforova


    Chapter 7

    Legal Document Summarization using Hybrid model          

    Deekshitha, Nandhini K


    Chapter 8

    Concept Network using Network Text Analysis        

    Md Masum Billah, Dipanita Saha, Farzana Bhuiyan, Mohammed Kaosar


    Chapter 9

    Question Answering Versus Machine Reading Comprehension: Neural Machine Reading Comprehension using Transformer Models



    Chapter 10

    Online Subjective Question Answering System Necessity of Education System      

    Madhav A. Kankhar, Bharat A. Shelke, C. Namrata Mahender


    Dr. Sandeep Kumar is currently a professor at CHRIST (Deemed to be University), Bangalore. He recently completed his post-doctoral research at Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia, in sentiment analysis. He is an associate editor for Springer's Human-centric Computing and Information Sciences (HCIS) journal. He has published more than eighty research papers in various international journals/conferences and attended several national and international conferences and workshops. He has authored/edited seven books in the area of computer science. Also, he has been serving as General Chair of the International Conference on Communication and Computational Technologies (ICCCT 2021, 22, and 23) and the Congress on Intelligent Systems (CIS 2022 and 2023). His research interests include nature-inspired algorithms, swarm intelligence, soft computing, and computational intelligence.

    Dr. Abdul Khader Jilani Saudagar

    Abdul Khader Jilani Saudagar received the Bachelor of Engineering (B.E.), Master of Technology (M. Tech.), and Doctor of Philosophy (Ph.D.) degrees in computer science and engineering, in 2001, 2006, and 2010, respectively. He is currently working as an Associate Professor with the Information Systems Department, College of Computer and Information Sciences (CCIS), Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. He is also the Head of the Intelligent Interactive Systems Research Group (IISRG), CCIS. He has ten years of research and teaching experience at both undergraduate (UG) and postgraduate (PG) level. He was the Principal Investigator of the funded projects from KACST, the Deanship of Scientific Research (IMSIU), and is working as the Principal Investigator for the project titled ``Usage of modern technologies to predict emergence of infectious diseases and to detect outbreak of pandemics'' in grand challenge track, funded by the Ministry of Education, Saudi Arabia. He has published a number of research papers in international journals and conferences. His research interests include artificial image processing, information technology, databases, and web and mobile application development. He is associated as a member with various professional bodies, like ACM, IACSIT, IAENG, and ISTE. He is working as an editorial board member and a reviewer for many international journals.

    Dr. Muskan Garg

    Dr. Muskan Garg is working as a Postdoctoral Research Fellow at Mayo Clinic, Rochester, Minnesota. Prior to Mayo Clinic, she served as Postdoctoral Research Associate at University of Florida. Prior to UFL, she served as Assistant Professor at Computer science and engineering department at Thapar Institute of Engineering & Technology, India. She has completed her masters and doctorate from Panjab University, Chandigarh. Her previous research work is associated with applied network science in the field of Information Retrieval and Natural Language Processing for social media data. In general, she is interested in exploring the domain of causal analysis, semantic relations, and ethics over social media data; and mental health analysis on social media.