Graph Learning and Network Science for Natural Language Processing (eBook, PDF)

Redaktion: Garg, Muskan; Prasad, Rajesh; Gupta, Amit Kumar

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 272
• Erscheinungstermin: 28. Dezember 2022
• Englisch
• ISBN-13: 9781000789300
• Artikelnr.: 66360340

Autorenporträt

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.

Inhaltsangabe

1. Graph of Words Model for Natural Language Processing. 2. Application of
NLP Using Graph Approaches. 3. Graph-based Extractive Approach for English
and Hindi Text Summarization. 4. Graph Embeddings for Natural Language
Processing. 5. Natural Language Processing with Graph and Machine Learning
Algorithms-based Large-scale Text Document Summarization and Its
Applications. 6. Ontology and Knowledge Graphs for Semantic Analysis in
Natural Language Processing. 7. Ontology and Knowledge Graphs for Natural
Language Processing. 8 Perfect Coloring by HB Color Matrix Algorithm
Method. 9 Cross-lingual Word Sense Disambiguation Using Multilingual
Co-occurrence Graphs. 10 Study of Current Learning Techniques for Natural
Language Processing for Early Detection of Lung Cancer. 11 A Critical
Analysis of Graph Topologies for Natural Language Processing and Their
Applications. 12 Graph-based Text Document Extractive Summarization. 13
Applications of Graphical Natural Language Processing. 14 Analysis of
Medical Images Using Machine Learning Techniques.