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斯坦福2012年版经典自然语言处理(NLP)课程 by Dan Jurafsky, Chris Manning

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【Jurafsky&Manning的经典自然语言处理课程(2012)】《Natural Language Processing with Dan Jurafsky and Chris Manning, 2012 - YouTube》 http://t.cn/E7xhw4Z

自然语言处理

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(1.1) Intro to NLP

12:52

(1.2) Regular Expressions in Practical N

06:05

(1.2) Regular Expressions

11:26

(1.3) Word Tokenization

14:26

(1.4) Word Normalization and Stemming

11:48

(1.5) Sentence Segmentation

05:35

(2.1) Defining Minimum Edit Distance

07:06

(2.2) Computing Minimum Edit Distance

05:55

(2.3) Backtrace for Computing Alignments

05:56

(2.4) Weighted Minimum Edit Distance

02:48

(2.5) Minimum Edit Distance in Computati

09:31

(3.1) Introduction to N-grams

08:42

(3.2) Estimating N-gram Probabilities

09:39

(3.3) Evaluation and Perplexity

10:40

(3.4) Generalization and Zeros

05:16

(3.5) Smoothing- Add-One

06:31

(3.6) Interpolation

10:26

(3.7) Good-Turing Smoothing

15:36

(3.8) Kneser-Ney Smoothing

09:00

(4.1) The Spelling Correction Task

05:41

(4.2) The Noisy Channel Model of Spellin

19:31

(4.3) Real-Word Spelling Correction

09:20

(4.4) State of the Art Systems

07:11

(5.1) What is Text Classification?

08:13

(5.2) Naive Bayes

03:20

(5.3) Formalizing the Naive Bayes Classi

09:30

(5.4) Naive Bayes- Learning

05:23

(5.5) Naive Bayes- Relationship to Langu

04:36

(5.6) Multinomial Naive Bayes- A Worked

09:00

(5.7) Precision

16:17

(5.8) Text Classification- Evaluation

07:18

(5.9) Practical Issues in Text Classific

05:57

(6.1) What is Sentiment Analysis?

07:18

(6.2) Sentiment Analysis- A Baseline Alg

13:28

(6.3) Sentiment Lexicons

08:38

(6.4) Learning Sentiment Lexicons

14:47

(6.5) Other Sentiment Tasks

11:02

(7.1) Generative vs. Discriminative Mode

07:50

(7.2) Making Features from Text for Disc

18:13

(7.3) Feature-Based Linear Classifiers

13:35

(7.4) Building a Maxent Model- The Nuts

08:05

(7.5) Generative vs. Discriminative Mode

12:11

(7.6) Maximizing the Likelihood

10:30

(8.1) Introduction to Information Extrac

09:19

(8.2) Evaluation of Named Entity Recogni

06:36

(8.3) Sequence Models for Named Entity R

15:06

(8.4) Maximum Entropy Sequence Models

13:03

(9.1) What is Relation Extraction?

09:48

(9.2) Using Patterns to Extract Relation

06:17

(9.3) Supervised Relation Extraction

10:52

(9.4) Semi-Supervised and Unsupervised R

09:53

(10.1) The Maximum Entropy Model Present

12:15

(10.2) Feature Overlap-Feature Interacti

12:52

(10.3) Conditional Maxent Models for Cla

04:12

(10.4) Smoothing-Regularization-Priors f

29:25

(11.1) An Introduction to Parts of Speec

13:20

(11.2) Some Methods for Sequence Models

13:05

(12.2) Empirical-Data-Driven Approach to

07:12

(12.2) Syntactic Structure- Constituency

08:47

(12.3) The Exponential Problem in Parsin

14:32

(13.1) CFGs and PCFGs

15:30

(13.2) Grammar Transforms

12:06

(13.3) CKY Parsing

23:26

(13.4) CKY Example

21:26

(13.5) Constituency Parser Evaluation

09:47

(14.1) Lexicalization of PCFGs

07:04

(14.2) Charniaks Model

18:25

(14.3) PCFG Independence Assumptions

09:45

(14.4) The Return of Unlexicalized PCFGs

10:09

(14.5) Latent Variable PCFGs

12:08

(15.1) Dependency Parsing Introduction

10:26

(15.2) Greedy Transition-Based Parsing

31:06

(15.3) Dependencies Encode Relational St

07:21

(17.1) Introduction to Information Retri

09:17

(17.2) Term-Document Incidence Matrices

09:00

(17.3) The Inverted Index

10:43

(17.4) Query Processing with the Inverte

06:44

(17.5) Phrase Queries and Positional Ind

19:46

(18.1) Introducing Ranked Retrieval

04:28

(18.2) Scoring with the Jaccard Coeffici

05:07

(18.3) Term Frequency Weighting

06:01

(18.4) Inverse Document Frequency Weight

10:18

(18.5) TF-IDF Weighting

03:43

(18.6) The Vector Space Model

16:23

(18.7) Calculating TF-IDF Cosine Scores

12:48

(18.8) Evaluating Search Engines

09:03

(19.1) Word Senses and Word Relations

11:51

(19.2) WordNet and Other Online Thesauri

06:24

(19.3) Word Similarity and Thesaurus Met

16:18

(19.4) Word Similarity- Distributional S

13:15

(19.5) Word Similarity- Distributional S

08:16

(20.1) What is Question Answering?

07:29

(20.2) Answer Types and Query Formulatio

08:48

(20.3) Passage Retrieval and Answer Extr

06:39

(20.4) Using Knowledge in QA

04:26

(20.5) Advanced- Answering Complex Quest

04:54

(21.1) Introduction to Summarization

04:47

(21.2) Generating Snippets

07:36

(21.3) Evaluating Summaries_ ROUGE

05:03

(21.3) Evaluating Summaries- ROUGE

05:03

(21.4) Summarizing Multiple Documents

10:43

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