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Natural Language Processing, by Michael Collins, Columbia University

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2017-07-17 02:39:46

https://www.coursetalk.com/providers/coursera/courses/natural-language-processin Natural Language Processing, by Michael Collins, Columbia University

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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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