Introduction : why NLP is hard, why NLP is useful, classical problems.
Words: Structure (spellcheck, morphology using FSTs).
Words: Semantics (Basic ideas in Lexical Semantics, WordNet and WordNet based similarity measures, Distributional measures of similarity, Concept Mining using Latent Semantic Analysis).
Words: Semantics (Word Sense Disambiguation; supervised, unsupervised and semi-supervised approaches)
Words: Parts of Speech (POST using Brill's Tagger and HMMs)
Sentences: Basic ideas in compositional semantics, Classical Parsing (Bottom up, top down, Dynamic Programming: CYK parser)
Sentences: Parsing using Probabilistic Context Free Grammars and EM based approaches for learning PCFG parameters.
Language Modelling (basic ideas, smoothing techniques)
Machine Translation (rule based techniques, Statistical Machine Translation (SMT), parameter learning in SMT (IBM models) using EM)
Information Extraction: Introduction to Named Entity Recognition and Relation Extraction
Natural Language Generation: the potential of using ML for NLG
Additional topics: Advanced Language Modelling (including LDA), other applications like summarization, question answering
