| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 1: Introduction to the Course | Download |
| 2 | Lecture 2: What Do We Do in NLP | Download |
| 3 | Lecture 3: Why is NLP hard | Download |
| 4 | Lecture 4: Empirical Laws | Download |
| 5 | Lecture 5: Text Processing: Basics | Download |
| 6 | Lecture 6: Spelling Correction: Edit Distance | Download |
| 7 | Lecture 7: Weighted Edit Distance, Other Variations | Download |
| 8 | Lecture 8: Noisy Channel Model for Spelling Correction | Download |
| 9 | Lecture 9 : N-Gram Language Models | Download |
| 10 | Lecture 10: Evaluation of Language Models, Basic Smoothing | Download |
| 11 | Lecture 11: Tutorial I | Download |
| 12 | Lecture 12: Language Modeling: Advanced Smoothing Models | Download |
| 13 | Lecture 13: Computational Morphology | Download |
| 14 | Lecture 14: Finite - State Methods for Morphology | Download |
| 15 | Lecture 15: Introduction to POS Tagging | Download |
| 16 | Lecture 16: Hidden Markov Models for POS Tagging | Download |
| 17 | Lecture 17: Viterbi Decoding for HMM, Parameter Learning | Download |
| 18 | Lecture 18: Baum Welch Algorithm | Download |
| 19 | Lecture 19: Maximum Entropy Models - I | Download |
| 20 | Lecture 20: Maximum Entropy Models - II | Download |
| 21 | Lecture 21: Conditional Random Fields | Download |
| 22 | Lecture 22: Syntax - Introduction | Download |
| 23 | Lecture 23: Syntax - Parsing I | Download |
| 24 | Lecture 24: Syntax - CKY, PCFGs | Download |
| 25 | Lecture 25: PCFGs - Inside-Outside Probabilities | Download |
| 26 | Lecture 26: Inside-Outside Probabilities | Download |
| 27 | Lecture 27: Dependency Grammars and Parsing - Introduction | Download |
| 28 | Lecture 28 : Transition Based Parsing : Formulation | Download |
| 29 | Lecture 29 : Transition Based Parsing : Learning | Download |
| 30 | Lecture 30 : MST-Based Dependency Parsing | Download |
| 31 | Lecture 31: MST-Based Dependency Parsing : Learning | Download |
| 32 | Lecture 32: Distributional Semantics - Introduction | Download |
| 33 | Lecture 33: Distributional Models of Semantics | Download |
| 34 | Lecture 34: Distributional Semantics : Applications, Structured Models | Download |
| 35 | Lecture 35: Word Embeddings - Part I | Download |
| 36 | Lecture 36 : Word Embeddings - Part II | Download |
| 37 | Lecture 37: Lexical Semantics | Download |
| 38 | Lecture 38: Lexical Semantics - Wordnet | Download |
| 39 | Lecture 39 : Word Sense Disambiguation - I | Download |
| 40 | Lecture 40 : Word Sense Disambiguation - II | Download |
| 41 | Lecture 41 : Novel Word Sense detection | Download |
| 42 | Lecture 42 : Topic Models : Introduction | Download |
| 43 | Lecture 43 :Latent Dirichlet Allocation : Formulation | Download |
| 44 | Lecture 44 : Gibbs Sampling for LDA, Applications | Download |
| 45 | Lecture 45 : LDA Variants and Applications - I | Download |
| 46 | Lecture 46:LDA Variants and Applications - II | Download |
| 47 | Lecture 47 : Entity Linking - I | Download |
| 48 | Lecture 48 : Entity Linking - II | Download |
| 49 | Lecture 49 : Information Extraction - Introduction | Download |
| 50 | Lecture 50 : Relation Extraction | Download |
| 51 | Lecture 51 : Distant Supervision | Download |
| 52 | Lecture 52 : Text Summarization - LEXRANK | Download |
| 53 | Lecture 53 : Optimization based Approaches for Summarization | Download |
| 54 | Lecture 54 : Summarization Evaluation | Download |
| 55 | Lecture 55 : Text Classification - I | Download |
| 56 | Lecture 56 : Text Classification - II | Download |
| 57 | Lecture 57 : Tutorial II | Download |
| 58 | Lecture 58 : Tutorial III | Download |
| 59 | Lecture 59 : Tutorial IV | Download |
| 60 | Lecture 60 : Tutorial V | Download |
| 61 | Lecture 61 : Sentiment Analysis - Introduction | Download |
| 62 | Lecture 62 : Sentiment Analysis - Affective Lexicons | Download |
| 63 | Lecture 63 : Learning Affective Lexicons | Download |
| 64 | Lecture 64 : Computing with Affective Lexicons | Download |
| 65 | Lecture 65 : Aspect - Based Sentiment Analysis | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 1: Introduction to the Course | Download Verified |
| 2 | Lecture 2: What Do We Do in NLP | Download Verified |
| 3 | Lecture 3: Why is NLP hard | Download Verified |
| 4 | Lecture 4: Empirical Laws | Download Verified |
| 5 | Lecture 5: Text Processing: Basics | Download Verified |
| 6 | Lecture 6: Spelling Correction: Edit Distance | Download Verified |
| 7 | Lecture 7: Weighted Edit Distance, Other Variations | Download Verified |
| 8 | Lecture 8: Noisy Channel Model for Spelling Correction | Download Verified |
| 9 | Lecture 9 : N-Gram Language Models | Download Verified |
| 10 | Lecture 10: Evaluation of Language Models, Basic Smoothing | Download Verified |
| 11 | Lecture 11: Tutorial I | Download Verified |
| 12 | Lecture 12: Language Modeling: Advanced Smoothing Models | Download Verified |
| 13 | Lecture 13: Computational Morphology | Download Verified |
| 14 | Lecture 14: Finite - State Methods for Morphology | Download Verified |
| 15 | Lecture 15: Introduction to POS Tagging | Download Verified |
| 16 | Lecture 16: Hidden Markov Models for POS Tagging | Download Verified |
| 17 | Lecture 17: Viterbi Decoding for HMM, Parameter Learning | Download Verified |
| 18 | Lecture 18: Baum Welch Algorithm | Download Verified |
| 19 | Lecture 19: Maximum Entropy Models - I | Download Verified |
| 20 | Lecture 20: Maximum Entropy Models - II | Download Verified |
| 21 | Lecture 21: Conditional Random Fields | Download Verified |
| 22 | Lecture 22: Syntax - Introduction | Download Verified |
| 23 | Lecture 23: Syntax - Parsing I | Download Verified |
| 24 | Lecture 24: Syntax - CKY, PCFGs | Download Verified |
| 25 | Lecture 25: PCFGs - Inside-Outside Probabilities | Download Verified |
| 26 | Lecture 26: Inside-Outside Probabilities | Download Verified |
| 27 | Lecture 27: Dependency Grammars and Parsing - Introduction | Download Verified |
| 28 | Lecture 28 : Transition Based Parsing : Formulation | Download Verified |
| 29 | Lecture 29 : Transition Based Parsing : Learning | Download Verified |
| 30 | Lecture 30 : MST-Based Dependency Parsing | Download Verified |
| 31 | Lecture 31: MST-Based Dependency Parsing : Learning | Download Verified |
| 32 | Lecture 32: Distributional Semantics - Introduction | Download Verified |
| 33 | Lecture 33: Distributional Models of Semantics | Download Verified |
| 34 | Lecture 34: Distributional Semantics : Applications, Structured Models | Download Verified |
| 35 | Lecture 35: Word Embeddings - Part I | Download Verified |
| 36 | Lecture 36 : Word Embeddings - Part II | Download Verified |
| 37 | Lecture 37: Lexical Semantics | Download Verified |
| 38 | Lecture 38: Lexical Semantics - Wordnet | Download Verified |
| 39 | Lecture 39 : Word Sense Disambiguation - I | Download Verified |
| 40 | Lecture 40 : Word Sense Disambiguation - II | Download Verified |
| 41 | Lecture 41 : Novel Word Sense detection | Download Verified |
| 42 | Lecture 42 : Topic Models : Introduction | Download Verified |
| 43 | Lecture 43 :Latent Dirichlet Allocation : Formulation | Download Verified |
| 44 | Lecture 44 : Gibbs Sampling for LDA, Applications | Download Verified |
| 45 | Lecture 45 : LDA Variants and Applications - I | Download Verified |
| 46 | Lecture 46:LDA Variants and Applications - II | Download Verified |
| 47 | Lecture 47 : Entity Linking - I | Download Verified |
| 48 | Lecture 48 : Entity Linking - II | Download Verified |
| 49 | Lecture 49 : Information Extraction - Introduction | Download Verified |
| 50 | Lecture 50 : Relation Extraction | Download Verified |
| 51 | Lecture 51 : Distant Supervision | Download Verified |
| 52 | Lecture 52 : Text Summarization - LEXRANK | Download Verified |
| 53 | Lecture 53 : Optimization based Approaches for Summarization | Download Verified |
| 54 | Lecture 54 : Summarization Evaluation | Download Verified |
| 55 | Lecture 55 : Text Classification - I | Download Verified |
| 56 | Lecture 56 : Text Classification - II | Download Verified |
| 57 | Lecture 57 : Tutorial II | Download Verified |
| 58 | Lecture 58 : Tutorial III | Download Verified |
| 59 | Lecture 59 : Tutorial IV | Download Verified |
| 60 | Lecture 60 : Tutorial V | Download Verified |
| 61 | Lecture 61 : Sentiment Analysis - Introduction | Download Verified |
| 62 | Lecture 62 : Sentiment Analysis - Affective Lexicons | Download Verified |
| 63 | Lecture 63 : Learning Affective Lexicons | Download Verified |
| 64 | Lecture 64 : Computing with Affective Lexicons | Download Verified |
| 65 | Lecture 65 : Aspect - Based Sentiment Analysis | Download Verified |
| Sl.No | Chapter Name | Hindi |
|---|---|---|
| 1 | Lecture 1: Introduction to the Course | Download |
| 2 | Lecture 2: What Do We Do in NLP | Download |
| 3 | Lecture 3: Why is NLP hard | Download |
| 4 | Lecture 4: Empirical Laws | Download |
| 5 | Lecture 5: Text Processing: Basics | Download |
| 6 | Lecture 6: Spelling Correction: Edit Distance | Download |
| 7 | Lecture 7: Weighted Edit Distance, Other Variations | Download |
| 8 | Lecture 8: Noisy Channel Model for Spelling Correction | Download |
| 9 | Lecture 9 : N-Gram Language Models | Download |
| 10 | Lecture 10: Evaluation of Language Models, Basic Smoothing | Download |
| 11 | Lecture 11: Tutorial I | Download |
| 12 | Lecture 12: Language Modeling: Advanced Smoothing Models | Download |
| 13 | Lecture 13: Computational Morphology | Download |
| 14 | Lecture 14: Finite - State Methods for Morphology | Download |
| 15 | Lecture 15: Introduction to POS Tagging | Download |
| 16 | Lecture 16: Hidden Markov Models for POS Tagging | Download |
| 17 | Lecture 17: Viterbi Decoding for HMM, Parameter Learning | Download |
| 18 | Lecture 18: Baum Welch Algorithm | Download |
| 19 | Lecture 19: Maximum Entropy Models - I | Download |
| 20 | Lecture 20: Maximum Entropy Models - II | Download |
| 21 | Lecture 21: Conditional Random Fields | Download |
| 22 | Lecture 22: Syntax - Introduction | Download |
| 23 | Lecture 23: Syntax - Parsing I | Download |
| 24 | Lecture 24: Syntax - CKY, PCFGs | Download |
| 25 | Lecture 25: PCFGs - Inside-Outside Probabilities | Download |
| 26 | Lecture 26: Inside-Outside Probabilities | Download |
| 27 | Lecture 27: Dependency Grammars and Parsing - Introduction | Download |
| 28 | Lecture 28 : Transition Based Parsing : Formulation | Download |
| 29 | Lecture 29 : Transition Based Parsing : Learning | Download |
| 30 | Lecture 30 : MST-Based Dependency Parsing | Download |
| 31 | Lecture 31: MST-Based Dependency Parsing : Learning | Download |
| 32 | Lecture 32: Distributional Semantics - Introduction | Download |
| 33 | Lecture 33: Distributional Models of Semantics | Download |
| 34 | Lecture 34: Distributional Semantics : Applications, Structured Models | Download |
| 35 | Lecture 35: Word Embeddings - Part I | Download |
| 36 | Lecture 36 : Word Embeddings - Part II | Download |
| 37 | Lecture 37: Lexical Semantics | Download |
| 38 | Lecture 38: Lexical Semantics - Wordnet | Download |
| 39 | Lecture 39 : Word Sense Disambiguation - I | Download |
| 40 | Lecture 40 : Word Sense Disambiguation - II | Download |
| 41 | Lecture 41 : Novel Word Sense detection | Download |
| 42 | Lecture 42 : Topic Models : Introduction | Download |
| 43 | Lecture 43 :Latent Dirichlet Allocation : Formulation | Download |
| 44 | Lecture 44 : Gibbs Sampling for LDA, Applications | Download |
| 45 | Lecture 45 : LDA Variants and Applications - I | Download |
| 46 | Lecture 46:LDA Variants and Applications - II | Download |
| 47 | Lecture 47 : Entity Linking - I | Download |
| 48 | Lecture 48 : Entity Linking - II | Download |
| 49 | Lecture 49 : Information Extraction - Introduction | Download |
| 50 | Lecture 50 : Relation Extraction | Download |
| 51 | Lecture 51 : Distant Supervision | Download |
| 52 | Lecture 52 : Text Summarization - LEXRANK | Download |
| 53 | Lecture 53 : Optimization based Approaches for Summarization | Download |
| 54 | Lecture 54 : Summarization Evaluation | Download |
| 55 | Lecture 55 : Text Classification - I | Download |
| 56 | Lecture 56 : Text Classification - II | Download |
| 57 | Lecture 57 : Tutorial II | Download |
| 58 | Lecture 58 : Tutorial III | Download |
| 59 | Lecture 59 : Tutorial IV | Download |
| 60 | Lecture 60 : Tutorial V | Download |
| 61 | Lecture 61 : Sentiment Analysis - Introduction | Download |
| 62 | Lecture 62 : Sentiment Analysis - Affective Lexicons | Download |
| 63 | Lecture 63 : Learning Affective Lexicons | Download |
| 64 | Lecture 64 : Computing with Affective Lexicons | Download |
| 65 | Lecture 65 : Aspect - Based Sentiment Analysis | Download |
| Sl.No | Chapter Name | Tamil |
|---|---|---|
| 1 | Lecture 1: Introduction to the Course | Download |
| 2 | Lecture 2: What Do We Do in NLP | Download |
| 3 | Lecture 3: Why is NLP hard | Download |
| 4 | Lecture 4: Empirical Laws | Download |
| 5 | Lecture 5: Text Processing: Basics | Download |
| 6 | Lecture 6: Spelling Correction: Edit Distance | Download |
| 7 | Lecture 7: Weighted Edit Distance, Other Variations | Download |
| 8 | Lecture 8: Noisy Channel Model for Spelling Correction | Download |
| 9 | Lecture 9 : N-Gram Language Models | Download |
| 10 | Lecture 10: Evaluation of Language Models, Basic Smoothing | Download |
| 11 | Lecture 11: Tutorial I | Download |
| 12 | Lecture 12: Language Modeling: Advanced Smoothing Models | Download |
| 13 | Lecture 13: Computational Morphology | Download |
| 14 | Lecture 14: Finite - State Methods for Morphology | Download |
| 15 | Lecture 15: Introduction to POS Tagging | Download |
| 16 | Lecture 16: Hidden Markov Models for POS Tagging | Download |
| 17 | Lecture 17: Viterbi Decoding for HMM, Parameter Learning | Download |
| 18 | Lecture 18: Baum Welch Algorithm | Download |
| 19 | Lecture 19: Maximum Entropy Models - I | Download |
| 20 | Lecture 20: Maximum Entropy Models - II | Download |
| 21 | Lecture 21: Conditional Random Fields | Download |
| 22 | Lecture 22: Syntax - Introduction | Download |
| 23 | Lecture 23: Syntax - Parsing I | Download |
| 24 | Lecture 24: Syntax - CKY, PCFGs | Download |
| 25 | Lecture 25: PCFGs - Inside-Outside Probabilities | Download |
| 26 | Lecture 26: Inside-Outside Probabilities | Download |
| 27 | Lecture 27: Dependency Grammars and Parsing - Introduction | Download |
| 28 | Lecture 28 : Transition Based Parsing : Formulation | Download |
| 29 | Lecture 29 : Transition Based Parsing : Learning | Download |
| 30 | Lecture 30 : MST-Based Dependency Parsing | Download |
| 31 | Lecture 31: MST-Based Dependency Parsing : Learning | Download |
| 32 | Lecture 32: Distributional Semantics - Introduction | Download |
| 33 | Lecture 33: Distributional Models of Semantics | Download |
| 34 | Lecture 34: Distributional Semantics : Applications, Structured Models | Download |
| 35 | Lecture 35: Word Embeddings - Part I | Download |
| 36 | Lecture 36 : Word Embeddings - Part II | Download |
| 37 | Lecture 37: Lexical Semantics | Download |
| 38 | Lecture 38: Lexical Semantics - Wordnet | Download |
| 39 | Lecture 39 : Word Sense Disambiguation - I | Download |
| 40 | Lecture 40 : Word Sense Disambiguation - II | Download |
| 41 | Lecture 41 : Novel Word Sense detection | Download |
| 42 | Lecture 42 : Topic Models : Introduction | Download |
| 43 | Lecture 43 :Latent Dirichlet Allocation : Formulation | Download |
| 44 | Lecture 44 : Gibbs Sampling for LDA, Applications | Download |
| 45 | Lecture 45 : LDA Variants and Applications - I | Download |
| 46 | Lecture 46:LDA Variants and Applications - II | Download |
| 47 | Lecture 47 : Entity Linking - I | Download |
| 48 | Lecture 48 : Entity Linking - II | Download |
| 49 | Lecture 49 : Information Extraction - Introduction | Download |
| 50 | Lecture 50 : Relation Extraction | Download |
| 51 | Lecture 51 : Distant Supervision | Download |
| 52 | Lecture 52 : Text Summarization - LEXRANK | Download |
| 53 | Lecture 53 : Optimization based Approaches for Summarization | Download |
| 54 | Lecture 54 : Summarization Evaluation | Download |
| 55 | Lecture 55 : Text Classification - I | Download |
| 56 | Lecture 56 : Text Classification - II | Download |
| 57 | Lecture 57 : Tutorial II | Download |
| 58 | Lecture 58 : Tutorial III | Download |
| 59 | Lecture 59 : Tutorial IV | Download |
| 60 | Lecture 60 : Tutorial V | Download |
| 61 | Lecture 61 : Sentiment Analysis - Introduction | Download |
| 62 | Lecture 62 : Sentiment Analysis - Affective Lexicons | Download |
| 63 | Lecture 63 : Learning Affective Lexicons | Download |
| 64 | Lecture 64 : Computing with Affective Lexicons | Download |
| 65 | Lecture 65 : Aspect - Based Sentiment Analysis | Download |