1st Edition
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 NPL. 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.
Chapter 1. Graph of Words Model for Natural Language Processing
Sharayu Mirasdar and Mangesh Bedekar
Chapter 2. Application of NLP Using Graph Approaches
Narendra Singh Yadav, Siddharth Jain, Archit Gupta, and Devansh Srivastava
Chapter 3. Graph-based Extractive Approach for English and Hindi Text Summarization
Rekha Jain, Manisha Sharma, Pratistha Mathur, and Surabhi Bhatia
Chapter 4 Graph Embeddings for Natural Language Processing
Jyoti Gavhane, Rajesh Prasad, and Rajeev Kumar
Chapter 5 Natural Language Processing with Graph and Machine Learning Algorithms-based Large-scale Text Document Summarization and Its Applications.
Shaikh Ashfaq Amir, Pathan Mohd. Shafi, Dr Vinod. V. Kimbahune, and Vijaykumar S. Bidve
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing
Ujwala Bharambe, Chhaya Narvekar, and Prakash Andugula
Chapter 7 Ontology and Knowledge Graphs for Natural Language Processing
Jayashree Prasad, Rahesha Mulla, Namrata Naikwade, B. Suresh Kumar, and Suresh Shanmugasundaram
Chapter 8 Perfect Coloring by HB Color Matrix Algorithm Method
A. A. Bhange and H. R. Bhapkar
Chapter 9 Cross-lingual Word Sense Disambiguation Using Multilingual Co-occurrence Graphs
Neha Janu, Anjali Singh, , Meenakshi Nawal, Sunita Gupta, Tapesh Kumar, and Vijendra Singh
Chapter 10 Study of Current Learning Techniques for Natural Language Processing for Early Detection of Lung Cancer
Vanita D. Jadhav and Lalit V. Patil
Chapter 11 A Critical Analysis of Graph Topologies for Natural Language Processing and Their Applications
Meenakshi Nawal, Sunita Gupta, Neha Janu, and Carlos M. Travieso-Gonzalez
Chapter 12 Graph-based Text Document Extractive Summarization
Sheetal Sonawane
Chapter 13 Applications of Graphical Natural Language Processing
T.Nalini, S.V. Gayetri Devi, and K.G.S. Venkatesan
Chapter 14 Analysis of Medical Images Using Machine Learning Techniques
Nikita Jain, Mahesh Kumar Joshi, Vishal Jain, and Reena Sharma
Biography
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 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.
Amit Kumar Gupta is an Assistant Professor at Manipal University Jaipur, India, and has more than 15 years of teaching as well as research experience. He has published more than 50 international research papers in the reputetable journal of indexing Scopus. He has also been guest editor of nine Scopus indexed journals. He has edited one book for IGI Global and organized three international conferences sponsored by the All India Council for Technical Education and the third phase of the Technical Education Quality Improvement Programme. His research areas are information security, machine learning, NLP and operating system CPU scheduling.
Rajesh Prasad is a Professor of Computer Science and Engineering at MIT Art, Design and Technology University, Pune, India. He has more than 25 years of academic and research experience, during which he has been instrumental in developing course curriculums and contents. He is associated with several universities in different roles. He has a Ph.D. in Computer Engineering and 7 research scholars have been awarded Ph.D.s under his guidance. He has published more than 90 papers in international and national journals, and has 3 patents and 6 copyrights. His areas of interest include text and data analysis and speech processing. He has been associated with various industries for research collaborations. He is an active member of various professional societies.
