 | Graph Learning and Network Science for Natural Language Processing Subjects: Computer Science; Engineering & Technology; Mathematics & Statistics; Computer Engineering; Databases; Electrical & Electronic Engineering; Systems & Control Engineering; Statistics & Probability; Data Preparation & Mining; Communications & Information Processing; Machine Learning; Electrical Engineering Communications; Statistics; 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 the 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, semantics 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. Dr. Muskan Garg is a Postdoctoral research associate at University of Florida, whose research focuses on the problems in natural language processing, information retrieval, and social media analysis. She received her Masters and PhD from Panjab University, India. Prior to TIET. 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 natural language processing (including a new NLP course series). Her current research interests are causal inference, mental health on social media, event detection and sentiment analysis. Dr. Amit Kumar Gupta , Assistant Professor, Manipal University Jaipur, Rajasthan, India having more than 15 Years of Teaching as well as Research Experience. He has published more than 50 International research papers in the reputed journal of indexing Scopus. He has also been Guest editor of 9 Scopus indexed journals. He has edited one IGI global Book and conducted three international conferences sponsored by AICTE and TEQIP-III. His research areas are Information Security, Machine Learning, NLP and OS CPU Scheduling. Rajesh Prasad is working as Professor (CSE) at MIT Art, Design, and Technology University, Pune. He has more than 25 years of academic and research experience. He is instrumental in developing the curriculum and contents. He is associated with several universities in different roles. He is Ph.D. in Computer Engineering. 07 research scholars have been awarded Ph.D. under his guidance. He has published more than 90 papers in international and national journals along with 03 patents and 06 copyrights. His area of interest is in Text and Data Analysis, Speech Processing, etc. He has been associated with industries for research collaborations. He is an active member of various professional societies. His biography was published by Marquis Publication USA in Sept 2009. |
