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Natural Language Processing, by Michael Collins, Columbia University

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2017-07-17 02:39:46
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https://www.coursetalk.com/providers/coursera/courses/natural-language-processin Natural Language Processing, by Michael Collins, Columbia University
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视频选集
（1/95）
2 - 1 - Introduction (Part 1) (11-17)_s
11:18
2 - 2 - Introduction (Part 2) (10-28)_s
10:28
3 - 1 - Introduction to the Language Modeling Problem (Part 1) (6-17)_s
06:18
3 - 2 - Introduction to the Language Modeling Problem (Part 2) (7-12)_s
07:13
3 - 3 - Markov Processes (Part 1) (8-56)_s
08:56
3 - 4 - Markov Processes (Part 2) (6-28)_s
06:28
3 - 5 - Trigram Language Models (9-40)_s
09:41
3 - 6 - Evaluating Language Models- Perplexity (12-36)_s
12:37
4 - 1 - Linear Interpolation (Part 1) (7-46)_s
07:46
4 - 2 - Linear Interpolation (Part 2) (11-35)_s
11:36
4 - 3 - Discounting Methods (Part 1) (9-26)_s
09:26
4 - 4 - Discounting Methods (Part 2) (3-34)_s
03:35
5 - 1 - Summary (2-31)_s
02:32
6 - 1 - The Tagging Problem (10-01)
10:02
6 - 2 - Generative Models for Supervised Learning (8-57)
08:57
6 - 3 - Hidden Markov Models (HMMs)- Basic Definitions (12-00)
12:01
6 - 4 - Parameter Estimation in HMMs (13-16)
13:16
6 - 5 - The Viterbi Algorithm for HMMs (Part 1) (14-07)
14:08
6 - 6 - The Viterbi Algorithm for HMMs (Part 2) (3-31)
03:32
6 - 7 - The Viterbi Algorithm for HMMs (Part 3) (7-33)
07:33
6 - 8 - Summary (1-50)
01:50
7 - 1 - Introduction (0-28)
00:29
7 - 2 - Introduction to the Parsing Problem (Part 1) (10-37)
10:37
7 - 3 - Introduction to the Parsing Problem (Part 2) (4-20)
04:21
7 - 4 - Context-Free Grammars (Part 1) (12-11)
12:12
7 - 5 - Context-Free Grammars (Part 2) (2-22)
02:23
7 - 6 - A Simple Grammar for English (Part 1) (10-32)
10:32
7 - 7 - A Simple Grammar for English (Part 2) (5-30)
05:30
7 - 8 - A Simple Grammar for English (Part 3) (11-21)
11:22
7 - 9 - A Simple Grammar for English (Part 4) (2-20)
02:21
7 - 10 - Examples of Ambiguity (5-56)
05:56
8 - 1 - Introduction (1-12)
01:12
8 - 2 - Basics of PCFGs (Part 1) (9-43)
09:43
8 - 3 - Basics of PCFGs (Part 2) (8-26)
08:27
8 - 4 - The CKY Parsing Algorithm (Part 1) (7-31)
07:31
8 - 5 - The CKY Parsing Algorithm (Part 2) (13-22)
13:22
8 - 6 - The CKY Parsing Algorithm (Part 3) (10-07)
10:08
9 - 1 - Weaknesses of PCFGs (14-59)
14:59
10 - 1 - Introduction (00-17)
00:17
10 - 2 - Lexicalization of a Treebank (10-44)
10:44
10 - 3 - Lexicalized PCFGs- Basic Definitions (12-40)
12:41
10 - 4 - Parameter Estimation in Lexicalized PCFGs (Part 1) (5-28)
05:28
10 - 5 - Parameter Estimation in Lexicalized PCFGs (Part 2) (9-08)
09:08
10 - 6 - Evaluation of Lexicalized PCFGs (Part 1) (9-32)
09:33
10 - 7 - Evaluation of Lexicalized PCFGs (Part 2) (11-28)
11:28
15 - 1 - Introduction (0-47)
00:47
15 - 2 - Two Example Problems (11-19)
11:20
15 - 3 - Features in Log-Linear Models (Part 1) (13-56)
13:57
15 - 4 - Features in Log-Linear Models (Part 2) (10-13)
10:13
15 - 5 - Definition of Log-linear Models (Part 1) (11-50)
11:51
15 - 6 - Definition of Log-linear Models (Part 2) (3-45)
03:45
15 - 7 - Parameter Estimation in Log-linear Models (Part 1) (12-44)
12:45
15 - 8 - Parameter Estimation in Log-linear Models (Part 2) (4-13)
04:14
15 - 9 - Smoothing-Regularization in Log-linear Models (15-12)
15:13
16 - 1 - Introduction (1-41)
01:42
16 - 2 - Recap of the Tagging Problem (3-15)
03:16
16 - 3 - Independence Assumptions in Log-linear Taggers (8-32)
08:33
16 - 4 - Features in Log-Linear Taggers (13-21)
13:22
16 - 5 - Parameters in Log-linear Models (3-59)
04:00
16 - 6 - The Viterbi Algorithm for Log-linear Taggers (9-37)
09:37
16 - 7 - An Example Application (9-28)
09:28
16 - 8 - Summary (2-45)
02:46
17 - 1 - Introduction (0-47)
00:48
17 - 2 - Conditional History-based Models (7-14)
07:14
17 - 3 - Representing Trees as Decision Sequences (Part 1) (7-23)
07:23
17 - 4 - Representing Trees as Decision Sequences (Part 2) (10-20)
10:20
17 - 5 - Features, and Beam Search (12-10)
12:11
17 - 6 - Summary (1-12)
01:13
18 - 1 - Introduction (0-36)
00:37
18 - 2 - Word Cluster Representations (8-36)
08:36
18 - 3 - The Brown Clustering Algorithm (Part 1) (11-50)
11:50
18 - 4 - The Brown Clustering Algorithm (Part 2) (8-30)
08:31
18 - 5 - The Brown Clustering Algorithm (Part 3) (9-18)
09:18
18 - 6 - Clusters in NE Recognition (Part 1) (11-33)
11:34
18 - 7 - Clusters in NE Recognition (Part 2) (7-28)
07:28
19 - 1 - Introduction (0-30)
00:31
19 - 2 - Recap of History-based Models (7-11)
07:11
19 - 3 - Motivation for GLMs (6-34)
06:34
19 - 4 - Three Components of GLMs (14-39)
14:40
19 - 5 - GLMs for Parse Reranking (10-36)
10:37
19 - 6 - Parameter Estimation with the Perceptron Algorithm (6-11)
06:12
19 - 7 - Summary (3-01)
03:01
20 - 1 - Introduction (1-02)
01:03
20 - 2 - Recap of GLMs (7-40)
07:40
20 - 3 - GLMs for Tagging (Part 1) (5-26)
05:26
20 - 4 - GLMs for Tagging (Part 2) (7-35)
07:35
20 - 5 - GLMs for Tagging (Part 3) (7-06)
07:06
20 - 6 - GLMs for Tagging (Part 4) (6-00)
06:01
21 - 1 - Introduction (0-37)
00:37
21 - 2 - The Dependency Parsing Problem (Part 1) (5-21)
05:22
21 - 3 - The Dependency Parsing Problem (Part 2) (13-53)
13:54
21 - 4 - GLMs for Dependency Parsing (Part 1) (11-59)
12:00
21 - 5 - GLMs for Dependency Parsing (Part 2) (8-28)
08:29
21 - 6 - Experiments with GLMs for Dep. Parsing (5-38)
05:39
21 - 7 - Summary (2-50)
02:50

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