June 2026
Intermediate
400 pages
10h 11m
English
book
Composite Artificial Intelligence
by T. S. Arun Samuel, L. Jerart Julus, P. Kanimozhi, T. Ananth Kumar, S. Balamurugan
Content preview from Composite Artificial Intelligence
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2Composite AI in Natural Language Processing: A Paradigm Shift in Understanding and Generating Human Language
Narendran M.1*, M. Beema Meharaj2, D. Diana Julie3, Sowmya Banala4, G. Umadevi5 and A. Devi6
1Department of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu, India
2Department of Information and Technology, KCG College of Technology, Chennai, Tamilnadu, India
3Department of Artificial Intelligence and Data Science, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamilnadu, India
4Cybersecurity Engineer, BMW, Charlotte, NC, USA
5Department of Computer Science and Engineering, AGNI College of Technology (Autonomous), Chennai, Tamilnadu, India
6Department of ECE, IFET College of Engineering, Villupuram, Tamilnadu, India
Abstract
Natural language processing (NLP) has improved a great deal, but there exist roadblocks such as understanding contexts, processing in different languages, and reasoning on complex data. Composite AI, which combines a variety of AI paradigms such as machine learning, deep learning, symbolic AI, and hybrid systems, provides a solution for these challenges. Composite AI boosts NLP solutions such as sentiment analysis, document comprehension, summarization, chatbots, and machine translation using the power of various paradigms.
This chapter delves into the role of composite AI in NLP and its underlying facets, such as using deep learning for representation learning, symbolic AI for logical reasoning, and ...
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