[Coursera] Natural Language Processing (Dan Jurafsky and Chris Manning)
Dan Jurafsky and Chris Manning

Natural Language Processing (102 files)
1 - 1 - Course Introduction (14_11).mp4 12.85MB
10 - 1 - What is Relation Extraction_ (9_47).mp4 10.68MB
10 - 2 - Using Patterns to Extract Relations (6_17).mp4 6.37MB
10 - 3 - Supervised Relation Extraction (10_51).mp4 10.81MB
10 - 4 - Semi-Supervised and Unsupervised Relation Extraction (9_53).mp4 10.55MB
11 - 1 - The Maximum Entropy Model Presentation (12_14).mp4 18.12MB
11 - 2 - Feature Overlap_Feature Interaction (12_51).mp4 13.25MB
11 - 3 - Conditional Maxent Models for Classification (4_11).mp4 5.02MB
11 - 4 - Smoothing_Regularization_Priors for Maxent Models (29_24).mp4 30.19MB
12 - 1 - An Intro to Parts of Speech and POS Tagging (13_19).mp4 12.46MB
12 - 2 - Some Methods and Results on Sequence Models for POS Tagging (13_04).mp4 13.44MB
13 - 1 - Syntactic Structure_ Constituency vs Dependency (8_46).mp4 9.40MB
13 - 2 - Empirical_Data-Driven Approach to Parsing (7_11).mp4 7.59MB
13 - 3 - The Exponential Problem in Parsing (14_30).mp4 15.59MB
14 - 1 - Instructor Chat (9_02).mp4 24.93MB
15 - 1 - CFGs and PCFGs (15_29).mp4 17.45MB
15 - 2 - Grammar Transforms (12_05).mp4 12.63MB
15 - 3 - CKY Parsing (23_25).mp4 27.45MB
15 - 4 - CKY Example (21_52).mp4 24.58MB
15 - 5 - Constituency Parser Evaluation (9_45).mp4 11.18MB
16 - 1 - Lexicalization of PCFGs (7_03).mp4 7.47MB
16 - 2 - Charniak_'s Model (18_23).mp4 19.88MB
16 - 3 - PCFG Independence Assumptions (9_44).mp4 10.31MB
16 - 4 - The Return of Unlexicalized PCFGs (20_53).mp4 22.25MB
16 - 5 - Latent Variable PCFGs (12_07).mp4 13.16MB
17 - 1 - Dependency Parsing Introduction (10_25).mp4 11.69MB
17 - 2 - Greedy Transition-Based Parsing (31_05).mp4 32.88MB
17 - 3 - Dependencies Encode Relational Structure (7_20).mp4 7.59MB
18 - 1 - Introduction to Information Retrieval (9_16).mp4 9.50MB
18 - 2 - Term-Document Incidence Matrices (8_59).mp4 9.46MB
18 - 3 - The Inverted Index (10_42).mp4 11.23MB
18 - 4 - Query Processing with the Inverted Index (6_43).mp4 7.07MB
18 - 5 - Phrase Queries and Positional Indexes (19_45).mp4 21.60MB
19 - 1 - Introducing Ranked Retrieval (4_27).mp4 4.80MB
19 - 2 - Scoring with the Jaccard Coefficient (5_06).mp4 5.66MB
19 - 3 - Term Frequency Weighting (5_59).mp4 6.67MB
19 - 4 - Inverse Document Frequency Weighting (10_16).mp4 11.66MB
19 - 5 - TF-IDF Weighting (3_42).mp4 4.30MB
19 - 6 - The Vector Space Model (16_22).mp4 17.76MB
19 - 7 - Calculating TF-IDF Cosine Scores (12_47).mp4 13.88MB
19 - 8 - Evaluating Search Engines (9_02).mp4 9.24MB
2 - 1 - Regular Expressions (11_25).mp4 11.37MB
2 - 2 - Regular Expressions in Practical NLP (6_04).mp4 8.35MB
2 - 3 - Word Tokenization (14_26).mp4 13.07MB
2 - 4 - Word Normalization and Stemming (11_47).mp4 10.57MB
2 - 5 - Sentence Segmentation (5_31).mp4 5.21MB
20 - 1 - Word Senses and Word Relations (11_50).mp4 15.62MB
20 - 2 - WordNet and Other Online Thesauri (6_23).mp4 9.17MB
20 - 3 - Word Similarity and Thesaurus Methods (16_17).mp4 21.22MB
20 - 4 - Word Similarity_ Distributional Similarity I (13_14).mp4 15.76MB
20 - 5 - Word Similarity_ Distributional Similarity II (8_15).mp4 9.92MB
21 - 1 - What is Question Answering_ (7_28).mp4 9.32MB
21 - 2 - Answer Types and Query Formulation (8_47).mp4 10.61MB
21 - 3 - Passage Retrieval and Answer Extraction (6_38).mp4 8.05MB
21 - 4 - Using Knowledge in QA (4_25).mp4 5.53MB
21 - 5 - Advanced_ Answering Complex Questions (4_52).mp4 6.47MB
22 - 1 - Introduction to Summarization.mp4 6.31MB
22 - 2 - Generating Snippets.mp4 10.08MB
22 - 3 - Evaluating Summaries_ ROUGE.mp4 6.85MB
22 - 4 - Summarizing Multiple Documents.mp4 14.05MB
23 - 1 - Instructor Chat II (5_23).mp4 19.53MB
3 - 1 - Defining Minimum Edit Distance (7_04).mp4 6.92MB
3 - 2 - Computing Minimum Edit Distance (5_54).mp4 5.65MB
3 - 3 - Backtrace for Computing Alignments (5_55).mp4 5.80MB
3 - 4 - Weighted Minimum Edit Distance (2_47).mp4 2.97MB
3 - 5 - Minimum Edit Distance in Computational Biology (9_29).mp4 9.38MB
4 - 1 - Introduction to N-grams (8_41).mp4 8.01MB
4 - 2 - Estimating N-gram Probabilities (9_38).mp4 9.94MB
4 - 3 - Evaluation and Perplexity (11_09).mp4 10.06MB
4 - 4 - Generalization and Zeros (5_15).mp4 4.90MB
4 - 5 - Smoothing_ Add-One (6_30).mp4 6.34MB
4 - 6 - Interpolation (10_25).mp4 9.83MB
4 - 7 - Good-Turing Smoothing (15_35).mp4 14.09MB
4 - 8 - Kneser-Ney Smoothing (8_59).mp4 8.85MB
5 - 1 - The Spelling Correction Task (5_39).mp4 5.08MB
5 - 2 - The Noisy Channel Model of Spelling (19_30).mp4 18.65MB
5 - 3 - Real-Word Spelling Correction (9_19).mp4 8.98MB
5 - 4 - State of the Art Systems (7_10).mp4 6.93MB
6 - 1 - What is Text Classification_ (8_12).mp4 8.08MB
6 - 2 - Naive Bayes (3_19).mp4 3.41MB
6 - 3 - Formalizing the Naive Bayes Classifier (9_28).mp4 8.58MB
6 - 4 - Naive Bayes_ Learning (5_22).mp4 6.49MB
6 - 5 - Naive Bayes_ Relationship to Language Modeling (4_35).mp4 4.29MB
6 - 6 - Multinomial Naive Bayes_ A Worked Example (8_58).mp4 11.94MB
6 - 7 - Precision, Recall, and the F measure (16_16).mp4 16.48MB
6 - 8 - Text Classification_ Evaluation (7_17).mp4 12.11MB
6 - 9 - Practical Issues in Text Classification (5_56).mp4 6.88MB
7 - 1 - What is Sentiment Analysis_ (7_17).mp4 10.02MB
7 - 2 - Sentiment Analysis_ A baseline algorithm (13_27).mp4 13.82MB
7 - 3 - Sentiment Lexicons (8_37).mp4 11.09MB
7 - 4 - Learning Sentiment Lexicons (14_45).mp4 19.56MB
7 - 5 - Other Sentiment Tasks (11_01).mp4 15.23MB
8 - 1 - Generative vs. Discriminative Models (7_49).mp4 8.31MB
8 - 2 - Making features from text for discriminative NLP models (18_11).mp4 17.47MB
8 - 3 - Feature-Based Linear Classifiers (13_34).mp4 14.11MB
8 - 4 - Building a Maxent Model_ The Nuts and Bolts (8_04).mp4 8.17MB
8 - 5 - Generative vs. Discriminative models_ The problem of overcounting evidence (12_15).mp4 12.81MB
8 - 6 - Maximizing the Likelihood (10_29).mp4 10.31MB
9 - 1 - Introduction to Information Extraction (9_18).mp4 9.85MB
9 - 2 - Evaluation of Named Entity Recognition (6_34).mp4 7.08MB
9 - 3 - Sequence Models for Named Entity Recognition (15_05).mp4 14.83MB
9 - 4 - Maximum Entropy Sequence Models (13_01).mp4 13.95MB
Type: Course
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Bibtex:
@article{,
title= {[Coursera] Natural Language Processing (Dan Jurafsky and Chris Manning)},
journal= {},
author= {Dan Jurafsky and Chris Manning},
year= {2012},
url= {},
abstract= {},
keywords= {},
terms= {},
license= {},
superseded= {}
}

10 day statistics (82 downloads)

Average Time 3 hrs, 38 mins, 44 secs
Average Speed 89.68kB/s
Best Time 0 mins, 56 secs
Best Speed 21.02MB/s
Worst Time 3 days,19 hrs, 13 mins, 20 secs
Worst Speed 3.58kB/s
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