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 ProcessingSharayu Mirasdar andMangesh Bedekar Chapter 2.Application of NLP Using Graph ApproachesNarendra Singh Yadav, Siddharth Jain, Archit Gupta, and Devansh Srivastava Chapter 3.Graph-based Extractive Approach for English and Hindi Text SummarizationRekha Jain, Manisha Sharma, Pratistha Mathur,and Surabhi Bhatia Chapter 4Graph Embeddings for Natural Language ProcessingJyoti Gavhane, Rajesh Prasad, and Rajeev Kumar Chapter 5Natural 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 6Ontology and Knowledge Graphs for Semantic Analysis in Natural Language ProcessingUjwala Bharambe, Chhaya Narvekar, and Prakash Andugula Chapter 7Ontology and Knowledge Graphs for Natural Language ProcessingJayashree Prasad, Rahesha Mulla, Namrata Naikwade, B. Suresh Kumar, and Suresh Shanmugasundaram Chapter 8Perfect Coloring by HB Color Matrix Algorithm MethodA. A. Bhange andH. R. Bhapkar Chapter 9Cross-lingual Word Sense Disambiguation Using Multilingual Co-occurrence GraphsNeha Janu, Anjali Singh, , Meenakshi Nawal, Sunita Gupta, Tapesh Kumar, andVijendra Singh Chapter 10Study of Current Learning Techniques for Natural Language Processing for Early Detection of Lung CancerVanita D. Jadhav andLalit V. Patil Chapter 11A Critical Analysis of Graph Topologies for Natural Language Processing andTheir ApplicationsMeenakshi Nawal, Sunita Gupta, Neha Janu, and Carlos M. Travieso-Gonzalez Chapter 12Graph-based Text Document Extractive SummarizationSheetal Sonawane Chapter 13Applications of Graphical Natural Language ProcessingT.Nalini, S.V. Gayetri Devi,and K.G.S. Venkatesan Chapter 14Analysis of Medical Images Using Machine Learning TechniquesNikita Jain,Mahesh Kumar Joshi, Vishal Jain, andReena Sharma

Muskan Garg is a postdoctoral research associate at the University of Florida, USA, whose research focuses on the problemsof 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 inNLP (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 atManipal University Jaipur, India, and hasmore 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 ofnine Scopus indexed journals. He has edited one book for IGI Global andorganized 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 operatingsystem CPU scheduling. Rajesh Prasad is a Professorof Computer Science and Engineeringat MIT Art, Design and Technology University, Pune, India. He has more than 25 years of academic and research experience, during which he has beeninstrumental in developingcourse curriculums and contents. He is associated with several universities in different roles. Hehas a Ph.D. in Computer Engineering and7 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.