Video Lectures
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Week 1 - Introduction to Natural Language Processing
(collapsed, click to expand)- Completed Introduction (Part 1) (11:17)
- Completed Introduction (Part 2) (10:28)
Week 1 - The Language Modeling Problem
(collapsed, click to expand)- Completed
Introduction to the Language Modeling Problem (Part 1) (6:17)
Quiz Questions for Introduction to the Language Modeling Problem (Part 1) (6:17)Language Modeling for Introduction to the Language Modeling Problem (Part 1) (6:17)Subtitles (text) for Introduction to the Language Modeling Problem (Part 1) (6:17)Subtitles (srt) for Introduction to the Language Modeling Problem (Part 1) (6:17)Video (MP4) for Introduction to the Language Modeling Problem (Part 1) (6:17)
- Completed Introduction to the Language Modeling Problem (Part 2) (7:12)
- Completed Markov Processes (Part 1) (8:56)
- Completed Markov Processes (Part 2) (6:28)
- Completed Trigram Language Models (9:40)
- Completed Evaluating Language Models: Perplexity (12:36)
Week 1 - Parameter Estimation in Language Models
(collapsed, click to expand)- Completed Linear Interpolation (Part 1) (7:46)
- Completed Linear Interpolation (Part 2) (11:35)
- Completed Discounting Methods (Part 1) (9:26)
- Completed Discounting Methods (Part 2) (3:34)
Week 1 - Summary
(collapsed, click to expand)- Completed Summary (2:31)
Week 2 - Tagging Problems, and Hidden Markov Models
(collapsed, click to expand)- Completed The Tagging Problem (10:01)
- Completed Generative Models for Supervised Learning (8:57)
- Completed Hidden Markov Models (HMMs): Basic Definitions (12:00)
- Completed Parameter Estimation in HMMs (13:16)
- Completed The Viterbi Algorithm for HMMs (Part 1) (14:07)
- Completed The Viterbi Algorithm for HMMs (Part 2) (3:31)
- Completed The Viterbi Algorithm for HMMs (Part 3) (7:33)
- Completed Summary (1:50)
Week 3 - Parsing, and Context-Free Grammars
(collapsed, click to expand)- Completed Introduction (0:28)
- Completed Introduction to the Parsing Problem (Part 1) (10:37)
- Completed Introduction to the Parsing Problem (Part 2) (4:20)
- Completed Context-Free Grammars (Part 1) (12:11)
- Completed Context-Free Grammars (Part 2) (2:22)
- Completed A Simple Grammar for English (Part 1) (10:32)
- Completed A Simple Grammar for English (Part 2) (5:30)
- Completed A Simple Grammar for English (Part 3) (11:21)
- Completed A Simple Grammar for English (Part 4) (2:20)
- Completed Examples of Ambiguity (5:56)
- Introduction (1:12)
- Basics of PCFGs (Part 1) (9:43)
- Basics of PCFGs (Part 2) (8:26)
- The CKY Parsing Algorithm (Part 1) (7:31)
- The CKY Parsing Algorithm (Part 2) (13:22)
- The CKY Parsing Algorithm (Part 3) (10:07)
- Introduction (00:17)
- Lexicalization of a Treebank (10:44)
- Lexicalized PCFGs: Basic Definitions (12:40)
- Parameter Estimation in Lexicalized PCFGs (Part 1) (5:28)
- Parameter Estimation in Lexicalized PCFGs (Part 2) (9:08)
- Evaluation of Lexicalized PCFGs (Part 1) (9:32)
- Evaluation of Lexicalized PCFGs (Part 2) (11:28)
- Opening Comments (0:25)
- introduction (2:03)
- Challenges in MT (8:06)
- Classical Approaches to MT (Part 1) (8:02)
- Classical Approaches to MT (Part 2) (5:56)
- Introduction to Statistical MT (12:31)
- Introduction (3:24)
- IBM Model 1 (Part 1) (13:06)
- IBM Model 1 (Part 2) (9:01)
- IBM Model 2 (11:27)
- The EM Algorithm for IBM Model 2 (Part 1) (5:09)
- The EM Algorithm for IBM Model 2 (Part 2) (8:37)
- The EM Algorithm for IBM Model 2 (Part 3) (9:28)
- The EM Algorithm for IBM Model 2 (Part 4) (4:52)
- Summary (1:48)
- Introduction (0:41)
- Learning Phrases from Alignments (Part 1) (9:18)
- Learning Phrases from Alignments (Part 2) (7:01)
- Learning Phrases from Alignments (Part 3) (8:47)
- A Sketch of Phrase-based Translation (8:17)
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Definition of the Decoding Problem (Part 1) (9:12)
Decoding Phrase Based for Definition of the Decoding Problem (Part 1) (9:12)Decoding Phrase Based Slides for Definition of the Decoding Problem (Part 1) (9:12)Subtitles (text) for Definition of the Decoding Problem (Part 1) (9:12)Subtitles (srt) for Definition of the Decoding Problem (Part 1) (9:12)Video (MP4) for Definition of the Decoding Problem (Part 1) (9:12)
- Definition of the Decoding Problem (Part 2) (13:00)
- Definition of the Decoding Problem (Part 3) (10:43)
- The Decoding Algorithm (Part 1) (14:39)
- The Decoding Algorithm (Part 2) (6:23)
- The Decoding Algorithm (Part 3) (12:29)
- Introduction (0:47)
- Two Example Problems (11:19)
- Features in Log-Linear Models (Part 1) (13:56)
- Features in Log-Linear Models (Part 2) (10:13)
- Definition of Log-linear Models (Part 1) (11:50)
- Definition of Log-linear Models (Part 2) (3:45)
- Parameter Estimation in Log-linear Models (Part 1) (12:44)
- Parameter Estimation in Log-linear Models (Part 2) (4:13)
- Smoothing/Regularization in Log-linear Models (15:12)
- Introduction (1:41)
- Recap of the Tagging Problem (3:15)
- Independence Assumptions in Log-linear Taggers (8:32)
- Features in Log-Linear Taggers (13:21)
- Parameters in Log-linear Models (3:59)
- The Viterbi Algorithm for Log-linear Taggers (9:37)
- An Example Application (9:28)
- Summary (2:45)
- Introduction (0:47)
- Conditional History-based Models (7:14)
- Representing Trees as Decision Sequences (Part 1) (7:23)
- Representing Trees as Decision Sequences (Part 2) (10:20)
- Features, and Beam Search (12:10)
- Summary (1:12)
- Introduction (0:36)
- Word Cluster Representations (8:36)
- The Brown Clustering Algorithm (Part 1) (11:50)
- The Brown Clustering Algorithm (Part 2) (8:30)
- The Brown Clustering Algorithm (Part 3) (9:18)
- Clusters in NE Recognition (Part 1) (11:33)
- Clusters in NE Recognition (Part 2) (7:28)
- Introduction (0:30)
- Recap of History-based Models (7:11)
- Motivation for GLMs (6:34)
- Three Components of GLMs (14:39)
- GLMs for Parse Reranking (10:36)
- Parameter Estimation with the Perceptron Algorithm (6:11)
- Summary (3:01)