Course Objectives & Syllabus Basics of NLP, Morphology, Tokenization, N-gram Models POS Tagging, Parsing, Treebank’s, Ambiguity Handling Word Sense Disambiguation, Semantic Parsing, Sentiment Analysis Machine Translation, Transformers, BERT/GPT, Ethical NLP Speech Recognition, Feature Extraction, Discourse Analysis UNIT I: Introduction to NLP Introduction to NLP: Origins and Challenges, Language and Grammar in NLP, Regular Expressions and Finite-State Automata, Tokenization: Text Segmentation and Sentence Splitting, Morphological Parsing: Stemming and Lemmatization, Spelling Error Detection and Correction, Minimum Edit Distance and Applications, Statistical Language Models:
Unigram, Bigram, and Trigram Models, Processing Indian Languages in NLP.