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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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2018-11-12 10:12:25
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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
NLP
AI
自然语言处理
机器学习
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视频选集
（1/101）
(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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