12:41

mod12lec62

26:56

mod12lec61

20:36

mod12lec60

23:08

mod12lec59

20:07

mod12lec58

20:51

mod11lec60

22:32

mod11lec59

24:34

Tutorial III

18:25

Tutorial II

29:32

mod11lec56

27:56

mod11lec55

20:36

mod11lec54

30:30

mod11lec53

32:38

mod11lec52

27:18

mod10lec51

29:17

mod10lec50

25:28

mod10lec49

28:12

mod10lec48

28:38

mod10lec47

23:12

Lecture 42 : Topic Models : Introduction

31:28

Lecture 46:LDA Variants and Applications - II

34:19

Lecture 45 : LDA Variants and Applications - I

29:04

Lecture 44 : Gibbs Sampling for LDA, Applications

37:20

Lecture 43 :Latent Dirichlet Allocation : Formulation

14:36

Lecture 41 : Novel Word Sense detection

30:07

Lecture 40 : Word Sense Disambiguation - II

33:21

Lecture 39 : Word Sense Disambiguation - I

30:01

Lecture 37: Lexical Semantics

42:38

Lecture 38: Lexical Semantics - Wordnet

31:58

Lecture 36 : Word Embeddings - Part II

22:07

Lecture 35: Word Embeddings - Part I

37:59

Lecture 34: Distributional Semantics : Applications, Structured Models

34:47

Lecture 33: Distributional Models of Semantics

26:16

Lecture 32: Distributional Semantics - Introduction

26:28

Lecture 31: MST-Based Dependency Parsing : Learning

33:11

Lecture 30 : MST-Based Dependency Parsing

36:38

Lecture 29 : Transition Based Parsing : Learning

29:46

Lecture 28 : Transition Based Parsing : Formulation

25:15

Lecture 27: Dependency Grammars and Parsing - Introduction

24:59

Lecture 26: Inside-Outside Probabilities

34:13

Lecture 25: PCFGs - Inside-Outside Probabilities

25:42

Lecture 24: Syntax - CKY, PCFGs

29:04

Lecture 23: Syntax - Parsing I

26:27

Lecture 22: Syntax - Introduction

27:03

Lecture 21: Conditional Random Fields

33:36

Lecture 20: Maximum Entropy Models - II

41:43

Lecture 12: Language Modeling: Advanced Smoothing Models

34:09

Lecture 10: Evaluation of Language Models, Basic Smoothing

27:57

Lecture 9 : N-Gram Language Models

32:21

Lecture 13: Computational Morphology

25:41

Lecture 14: Finite - State Methods for Morphology

29:15

Lecture 15: Introduction to POS Tagging

28:21

Lecture 16: Hidden Markov Models for POS Tagging

32:33

Lecture 17: Viterbi Decoding for HMM, Parameter Learning

32:09

Lecture 18: Baum Welch Algorithm

37:57

Lecture 19: Maximum Entropy Models - I

28:23

Week 2 Tutorial

34:40

Lecture 8: Noisy Channel Model for Spelling Correction

32:42

Lecture 6: Spelling Correction: Edit Distance

29:09

Lecture 7: Weighted Edit Distance, Other Variations