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
