This is a comprehensive natural language processing (NLP) guide, covering key techniques like sentiment analysis, machine translation, and named entity recognition. It explores deep learning models, and practical applications in multiple sectors, and is perfect for students and professionals looking to master NLP's transformative power.
This is a comprehensive natural language processing (NLP) guide, covering key techniques like sentiment analysis, machine translation, and named entity recognition. It explores deep learning models, and practical applications in multiple sectors, and is perfect for students and professionals looking to master NLP's transformative power.
Dr. Xiaochun Cheng is at Swansea University, Wales, UK, specializing in artificial intelligence, data science, and natural language processing (NLP). With extensive experience in computational linguistics, Dr. Cheng has published numerous papers in top-tier journals and actively contributes to advancing AI research. Dr. Preethi Nanjundan is an Associate Professor and Research head at CHRIST University, Pune Lavasa Campus, India, with 20 years of experience in research and teaching. Her expertise spans machine learning, neural networks, and NLP, and she has published over 40 papers and several books. She is also a lifetime member of professional bodies like IEEE and CSI. Dr. Jossy P George is a distinguished professor in the Department of Data Science and the Dean and Director at CHRIST University, Delhi NCR Campus, India. His research interests include data analytics, machine learning, and NLP, with significant contributions to both academia and industry. He plays a pivotal role in mentoring students and leading collaborative research projects.
Inhaltsangabe
1. Introduction 2. Parsing and Syntax 3. Smoothed Estimation and Language Modelling 4. Semantic Analysis and Discourse Processing 5. Natural Language Generation and Machine Translation 6. Information Retrieval and Lexical Resurces 7. Unsupervised Methods in NLP 8. Summary 9. Conclusion
1. Introduction 2. Parsing and Syntax 3. Smoothed Estimation and Language Modelling 4. Semantic Analysis and Discourse Processing 5. Natural Language Generation and Machine Translation 6. Information Retrieval and Lexical Resurces 7. Unsupervised Methods in NLP 8. Summary 9. Conclusion
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