NOC:Natural Language Processing


Lecture 1 - Introduction to the Course


Lecture 2 - What Do We Do in NLP


Lecture 3 - Why is NLP hard


Lecture 4 - Empirical Laws


Lecture 5 - Text Processing: Basics


Lecture 6 - Spelling Correction: Edit Distance


Lecture 7 - Weighted Edit Distance, Other Variations


Lecture 8 - Noisy Channel Model for Spelling Correction


Lecture 9 - N-Gram Language Models


Lecture 10 - Evaluation of Language Models, Basic Smoothing


Lecture 11 - Tutorial I


Lecture 12 - Language Modeling: Advanced Smoothing Models


Lecture 13 - Computational Morphology


Lecture 14 - Finite - State Methods for Morphology


Lecture 15 - Introduction to POS Tagging


Lecture 16 - Hidden Markov Models for POS Tagging


Lecture 17 - Viterbi Decoding for HMM, Parameter Learning


Lecture 18 - Baum Welch Algorithm


Lecture 19 - Maximum Entropy Models - I


Lecture 20 - Maximum Entropy Models - II


Lecture 21 - Conditional Random Fields


Lecture 22 - Syntax - Introduction


Lecture 23 - Syntax - Parsing I


Lecture 24 - Syntax - CKY, PCFGs


Lecture 25 - PCFGs - Inside-Outside Probabilities


Lecture 26 - Inside-Outside Probabilities


Lecture 27 - Dependency Grammars and Parsing - Introduction


Lecture 28 - Transition Based Parsing : Formulation


Lecture 29 - Transition Based Parsing : Learning


Lecture 30 - MST-Based Dependency Parsing


Lecture 31 - MST-Based Dependency Parsing : Learning


Lecture 32 - Distributional Semantics - Introduction


Lecture 33 - Distributional Models of Semantics


Lecture 34 - Distributional Semantics : Applications, Structured Models


Lecture 35 - Word Embeddings - Part I


Lecture 36 - Word Embeddings - Part II


Lecture 37 - Lexical Semantics


Lecture 38 - Lexical Semantics - Wordnet


Lecture 39 - Word Sense Disambiguation - I


Lecture 40 - Word Sense Disambiguation - II


Lecture 41 - Novel Word Sense detection


Lecture 42 - Topic Models : Introduction


Lecture 43 - Latent Dirichlet Allocation : Formulation


Lecture 44 - Gibbs Sampling for LDA, Applications


Lecture 45 - LDA Variants and Applications - I


Lecture 46 - LDA Variants and Applications - II


Lecture 47 - Entity Linking - I


Lecture 48 - Entity Linking - II


Lecture 49 - Information Extraction - Introduction


Lecture 50 - Relation Extraction


Lecture 51 - Distant Supervision


Lecture 52 - Text Summarization - LEXRANK


Lecture 53 - Optimization based Approaches for Summarization


Lecture 54 - Summarization Evaluation


Lecture 55 - Text Classification - I


Lecture 56 - Text Classification - II


Lecture 57 - Tutorial II


Lecture 58 - Tutorial III


Lecture 59 - Tutorial IV


Lecture 60 - Tutorial V


Lecture 61 - Sentiment Analysis - Introduction


Lecture 62 - Sentiment Analysis - Affective Lexicons


Lecture 63 - Learning Affective Lexicons


Lecture 64 - Computing with Affective Lexicons


Lecture 65 - Aspect - Based Sentiment Analysis