Muskan Garg - Graph Learning and Network Science for Natural Language Processing

Graph Learning and Network Science for Natural Language Processing

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NLP. It also contains information about language generation based on graphical theories and language models.

Features:

• Presents a comprehensive study of the interdisciplinary graphical approach to NLP
• Covers recent computational intelligence techniques for graph-based neural network models
• Discusses advances in random walk-based techniques, semantic webs, and lexical networks
• Explores recent research into NLP for graph-based streaming data
• Reviews advances in knowledge graph embedding and ontologies for NLP approaches

This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

Series Editor: Vishal Jain

The objective of this series is to provide researchers a platform to present state of the art innovations, research, and design and implement methodological and algorithmic solutions to data processing problems, designing and analyzing evolving trends in health informatics and computer-aided diagnosis. This series provides support and aid to researchers involved in designing decision support systems that will permit societal acceptance of ambient intelligence. The overall goal of this series is to present the latest snapshot of ongoing research as well as to shed further light on future directions in this space. The series presents novel technical studies as well as position and vision papers comprising hypothetical/speculative scenarios. The book series seeks to compile all aspects of computational intelligence techniques from fundamental principles to current advanced concepts. For this series, we invite researchers, academicians and professionals to contribute, expressing their ideas and research in the application of intelligent techniques to the field of engineering in handbook, reference, or monograph volumes.

Computational Intelligence Techniques and Their Applications to Software Engineering Problems

Ankita Bansal, Abha Jain, Sarika Jain, Vishal Jain, and Ankur Choudhary

Smart Computational Intelligence in Biomedical and Health Informatics

Amit Kumar Manocha, Mandeep Singh, Shruti Jain, and Vishal Jain

Data Driven Decision Making Using Analytics

Parul Gandhi, Surbhi Bhatia, and Kapal Dev

Smart Computing and Self-Adaptive Systems

Simar Preet Singh, Arun Solanki, Anju Sharma, Zdzislaw Polkowski, and Rajesh Kumar

Advancing Computational Intelligence Techniques for Security Systems Design

Uzzal Sharma, Parmanand Astya, Anupam Baliyan, Salah-ddine Krit, Vishal Jain, and Mohammad Zubair Kha

Graph Learning and Network Science for Natural Language Processing

Edited by Muskan Garg, Amit Kumar Gupta, and Rajesh Prasad

For more information about this series, please visit: www.routledge.com/Computational-Intelligence-Techniques/book-series/CIT

Graph Learning and Network Science for Natural Language Processing

Edited by

Muskan Garg, Amit Kumar Gupta and Rajesh Prasad

MATLAB and Simulink are trademarks of the MathWorks, Inc. and are used with permission. The MathWorks does not warrant the accuracy f the text or exercises in this book. This books use or discussion of MATLAB and Simulink software or related products does not constitute endorsement or sponsorship by the MathWorks of a particular pedagogical approach or particular use of the MATLAB and Simulink software.

Cover image: Shutterstock

First edition published 2023

by CRC Press

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and by CRC Press

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CRC Press is an imprint of Taylor & Francis Group, LLC

2023 selection and editorial matter, Muskan Garg, Amit Kumar Gupta and Rajesh Prasad; individual chapters, the contributors

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ISBN: 9781032224565 (hbk)

ISBN: 9781032224572 (pbk)

ISBN: 9781003272649 (ebk)

DOI: 10.1201/9781003272649

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by Deanta Global Publishing Services, Chennai, India

Sharayu Mirasdar and Mangesh Bedekar

Narendra Singh Yadav, Siddharth Jain, Archit Gupta, and Devansh Srivastava

Rekha Jain, Manisha Sharma, Pratistha Mathur, and Surbhi Bhatia

Jyoti Gavhane, Rajesh Prasad, and Rajeev Kumar

Shaikh Ashfaq Amir, Pathan Mohd. Shafi, Vinod V. Kimbahune, and Vijaykumar S. Bidve

Ujwala Bharambe, Chhaya Narvekar, and Prakash Andugula

Jayashree Prasad, Rahesha Mulla, Namrata Naikwade, B. Suresh Kumar, and Suresh Shanmugasundaram

A. A. Bhange and H. R. Bhapkar

Neha Janu, Anjali Singh, Meenakshi Nawal, Sunita Gupta, Tapesh Kumar, and Vijendra Singh

Vanita D. Jadhav and Lalit V. Patil

Meenakshi Nawal, Sunita Gupta, Neha Janu, and Carlos M. Travieso-Gonzalez

Sheetal Sonawane

S. V. Gayetri Devi, T. Nalini, and K. G. S. Venkatesan

Nikita Jain, Mahesh Kumar Joshi, Vishal Jain, and Manish Dubey

Muskan Garg is a postdoctoral research associate at the University of Florida, USA whose research focuses on the problems of natural language processing (NLP), information retrieval, and social media analysis. She received her Masters and Ph.D. from Panjab University, India. Her current focus is on research and development of cutting-edge NLP approaches to solving problems of national and international importance and on initiation and broadening of a new program in NLP (including a new NLP course series). Her current research interests are causal inference, mental health on social media, event detection, and sentiment analysis.