Key Features
"• Discusses comparison of language models, basic vector models and neural language models • Introduces basic concepts of word, morphology and semantics • Explains word embedding and deep learning models including pre-trained models • Emphasizes on machine learning and deep learning approaches to NLP tasks – part-of speech tagging, syntactic processing, semantic processing and discourse and dialog systems including the latest ChatGPT architecture • Illustrates NLP from an application perspective – machine learning and deep learning approaches to text categorization, machine translation, information extraction, question answering and summarization • Covers ethics of NLP including bias and fairness • Rich pedagogy – objective-type questions, activities, case-studies and project-based learning exercises."