Origins and challenges of NLP – Language Modelling: Grammar-based LM, Statistical LM - Regular Expressions, Tokenization, Word and Sentence Segmentation, Detecting and Correcting Spelling Errors, Edit Distance-weighted edit distance, dynamic programming edit distance, Introduction to NLTK

N-grams – Computing unigram, bigram, trigram probabilities, Evaluating N-grams, Smoothing, Interpolation and Backoff – WordClasses, Part-of-Speech Tagging, Rule-based, Stochastic and Transformation-based tagging, Issues in PoS tagging – Hidden Markov and Maximum Entropy models, Conditional random fields (CRF)

Context-Free Grammars, Grammar rules for English, Treebanks, Normal Forms forgrammar – Dependency Grammar – Syntactic Parsing, Ambiguity, Dynamic Programming parsing – Shallow parsing – Probabilistic CFG, Probabilistic CYK, Probabilistic Lexicalized CFGs - Feature structures, Unification of feature structures.

Introduction to lexical semantics, Syntax-DrivenSemantic analysis, Semantic attachments – Word Senses, Relations between Senses,Thematic Roles, selectional restrictions – Word Sense Disambiguation, Wordnet-Synsets, lemma vs synsets, Dictionary & Thesaurus, Bootstrapping methods – Word Similarity usingThesaurus and Distributional methods

Grammar Checkers Dictation, Automatic Document Generation, NL Interfaces, Text Summarization, Information Extraction, Sentiment Mining, Machine Translation, Contemporary Issues.