Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This new volume, Natural Language Processing in Artificial Intelligence, focuses on natural language processing (NLP), artificial intelligence (AI), and allied areas, discussing theoretical work and advanced applications, approaches, and techniques for computational models of information and how they are presented by language (artificial, human, or natural in other ways). It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It also explores difficult problems and challenges related to partiality, under specification, and context-dependency, which are signature features of information in nature and natural languages.
Topics include the process of business intelligence and how this platform is used, the concepts of information retrieval systems, the neural machine translation (NMT) process, the choice of words and text in natural language processing, embedded traffic control and management systems, a technique for generating ontology by adopting the fruit fly optimization algorithm, POS labeling using the Viterbi algorithm, how natural language processing techniques can be used to prevent phishing attacks, and more.
Key features:
- Addresses the functional frameworks and workflow that are trending in NLP and AI
- Explores basic and high-level concepts, thus serving as a resource for those in the industry while also helping beginners to understand both basic and advanced aspects
- Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI
- Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world
- Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP
This volume will be a useful and informative resource for faculty, advanced-level students, and professionals in the field of artificial intelligence, natural language processing, and other areas.
Table of Contents:
1. Natural Language Processing and Artificial Intelligence: A Perspective Towards Current Trends, Challenges, and Applications 2. Artificial Intelligence-Based NLP: Methods, Trends, and Challenges 3. Exploring the Synergy of Machine Learning and Natural Language Processing 4. Navigating the AI Landscape: Architectures and Algorithms for Natural Language Processing 5. Bridging Language Structure and Deep Learning Models: A Comprehensive Exploration of Natural Language Processing 6. Artificial Intelligence Architectures and Algorithms in Natural Language Processing Ecosystems 7. Classification of Real and Fake News Using Machine Learning and Deep Learning Techniques 8. Unleashing the Power of Big Data: Information Retrieval and Text Mining Strategies 9. Reconciliation of Emotional Intelligence with Artificial Intelligence for Augmenting Organizational Effectiveness: A Conceptual Model 10. Innovations in Future Mobility and Intelligent Virtual Assistants: Lensing Machine Learning AI-Based Chatbots for Digital Users 11. FedNLP: Secure and Efficient Federated Learning Using NLP: Architecture, Challenges, and Solutions 12. Large Language Model (LLM)-Based Human-Like Content Creation, Including Articles, Social Media Posts, and Marketing Content 13. LLM-Powered Chatbots and Virtual Assistants for Interactive and Human-Like Interactions 14. TrOCR-Med: Revolutionizing Medical Data Handling with Transformer-Based Handwritten Optical Character Recognition and Extraction 15. Speech Emotion Detection Using Deep Learning
