Natural-Language-Process-KOpZ
1 - 1 - Course Introduction (14_11).mp412.26MB
10 - 1 - What is Relation Extraction_ (9_47).mp410.19MB
10 - 2 - Using Patterns to Extract Relations (6_17).mp46.08MB
10 - 3 - Supervised Relation Extraction (10_51).mp410.31MB
10 - 4 - Semi-Supervised and Unsupervised Relation Extraction (9_53).mp410.06MB
11 - 1 - The ximum Entropy Model Presentation (12_14).mp417.28MB
11 - 2 - Feature Overlap_Feature Interaction (12_51).mp412.63MB
11 - 3 - Conditional xent Models for Classification (4_11).mp44.79MB
11 - 4 - oothing_Regularization_Priors for xent Models (29_24).mp428.80MB
12 - 1 - An Intro to Parts of Speech and POS Tagging (13_19).mp411.88MB
12 - 2 - Some Methods and Results on Sequence Models for POS Tagging (13_04).mp412.82MB
13 - 1 - Syntactic Structure_ Constituency vs Dependency (8_46).mp48.96MB
13 - 2 - Empirical_Data-Driven Approach to Parsing (7_11).mp47.24MB
13 - 3 - The Exponential Problem in Parsing (14_30).mp414.87MB
14 - 1 - Instructor Chat (9_02).mp423.78MB
15 - 1 - CFGs and PCFGs (15_29).mp416.65MB
15 - 2 - Gramr Transforms (12_05).mp412.05MB
15 - 3 - CKY Parsing (23_25).mp426.18MB
15 - 4 - CKY Example (21_52).mp423.44MB
15 - 5 - Constituency Parser Evaluation (9_45).mp410.66MB
16 - 1 - Lexicalization of PCFGs (7_03).mp47.12MB
16 - 2 - Charniak_\s Model (18_23).mp418.96MB
16 - 3 - PCFG Independence Assumptions (9_44).mp49.83MB
16 - 4 - The Return of Unlexicalized PCFGs (20_53).mp421.22MB
16 - 5 - Latent Variable PCFGs (12_07).mp412.55MB
17 - 1 - Dependency Parsing Introduction (10_25).mp411.15MB
17 - 2 - Greedy Transition-ba< x> sed Parsing (31_05).mp431.36MB
17 - 3 - Dependencies Encode Relational Structure (7_20).mp47.24MB
18 - 1 - Introduction to Infortion Retrieval (9_16).mp49.06MB
18 - 2 - Term-Document Incidence trices (8_59).mp49.02MB
18 - 3 - The Inverted Index (10_42).mp410.71MB
18 - 4 - Query Processing with the Inverted Index (6_43).mp46.74MB
18 - 5 - Phrase Queries and Positional Indexes (19_45).mp420.60MB
19 - 1 - Introducing Ranked Retrieval (4_27).mp44.58MB
19 - 2 - Scoring with the Jaccard Coefficient (5_06).mp45.39MB
19 - 3 - Term Frequency Weighting (5_59).mp46.36MB
19 - 4 - Inverse Document Frequency Weighting (10_16).mp411.12MB
19 - 5 - TF-IDF Weighting (3_42).mp44.10MB
19 - 6 - The Vector Space Model (16_22).mp416.93MB
19 - 7 - Calculating TF-IDF Cosine Scores (12_47).mp413.23MB
19 - 8 - Evaluating Search Engines (9_02).mp48.82MB
2 - 1 - Regular ex< x> pressions (11_25).mp410.85MB
2 - 2 - Regular ex< x> pressions in Practical NLP (6_04).mp47.96MB
2 - 3 - Word Tokenization (14_26).mp412.47MB
2 - 4 - Word Norlization and Stemming (11_47).mp410.08MB
2 - 5 - Sentence Segmentation (5_31).mp44.97MB
20 - 1 - Word Senses and Word Relations (11_50).mp414.89MB
20 - 2 - WordNet and Other Online Thesauri (6_23).mp48.75MB
20 - 3 - Word Similarity and Thesaurus Methods (16_17).mp420.24MB
20 - 4 - Word Similarity_ Distributional Similarity I (13_14).mp415.03MB
20 - 5 - Word Similarity_ Distributional Similarity II (8_15).mp49.46MB
21 - 1 - What is Question Answering_ (7_28).mp48.89MB
21 - 2 - Answer Types and Query Formulation (8_47).mp410.12MB
21 - 3 - Passage Retrieval and Answer Extraction (6_38).mp47.68MB
21 - 4 - Using Knowledge in QA (4_25).mp45.27MB
21 - 5 - Advanced_ Answering Complex Questions (4_52).mp46.17MB
22 - 1 - Introduction to Sumrization.mp46.02MB
22 - 2 - Generating Snippets.mp49.61MB
22 - 3 - Evaluating Sumries_ ROUGE.mp46.53MB
22 - 4 - Sumrizing Multiple Documents.mp413.40MB
23 - 1 - Instructor Chat II (5_23).mp418.63MB
3 - 1 - Defining Minimum Edit Distance (7_04).mp46.60MB
3 - 2 - Computing Minimum Edit Distance (5_54).mp45.38MB
3 - 3 - Backtrace for Computing Alignments (5_55).mp45.53MB
3 - 4 - Weighted Minimum Edit Distance (2_47).mp42.83MB
3 - 5 - Minimum Edit Distance in Computational Biology (9_29).mp48.95MB
4 - 1 - Introduction to N-grams (8_41).mp47.64MB
4 - 2 - Estiting N-gram Probabilities (9_38).mp49.48MB
4 - 3 - Evaluation and Perplexity (11_09).mp49.60MB
4 - 4 - Generalization and Zeros (5_15).mp44.67MB
4 - 5 - oothing_ Add-One (6_30).mp46.04MB
4 - 6 - Interpolation (10_25).mp49.38MB
4 - 7 - Good-Turing oothing (15_35).mp413.44MB
4 - 8 - Kneser-Ney oothing (8_59).mp48.44MB
5 - 1 - The Spelling Correction Task (5_39).mp44.84MB
5 - 2 - The sy Channel Model of Spelling (19_30).mp417.79MB
5 - 3 - Real-Word Spelling Correction (9_19).mp48.56MB
5 - 4 - State of the Art Systems (7_10).mp46.61MB
6 - 1 - What is Text Classification_ (8_12).mp47.70MB
6 - 2 - Naive Bayes (3_19).mp43.25MB
6 - 3 - Forlizing the Naive Bayes Classifier (9_28).mp48.19MB
6 - 4 - Naive Bayes_ Learning (5_22).mp46.18MB
6 - 5 - Naive Bayes_ Relationship to Language Modeling (4_35).mp44.09MB
6 - 6 - Multinomial Naive Bayes_ A Worked Example (8_58).mp411.38MB
6 - 7 - Precision Recall and the F measure (16_16).mp415.72MB
6 - 8 - Text Classification_ Evaluation (7_17).mp411.54MB
6 - 9 - Practical Issues in Text Classification (5_56).mp46.56MB
7 - 1 - What is Sentiment Analysis_ (7_17).mp49.56MB
7 - 2 - Sentiment Analysis_ A ba< x> seline algorithm (13_27).mp413.18MB
7 - 3 - Sentiment Lexicons (8_37).mp410.58MB
7 - 4 - Learning Sentiment Lexicons (14_45).mp418.65MB
7 - 5 - Other Sentiment Tasks (11_01).mp414.53MB
8 - 1 - Generative vs. Discriminative Models (7_49).mp47.92MB
8 - 2 - king features from text for discriminative NLP models (18_11).mp416.66MB
8 - 3 - Feature-ba< x> sed Linear Classifiers (13_34).mp413.46MB
8 - 4 - Building a xent Model_ The Nuts and Bolts (8_04).mp47.80MB
8 - 5 - Generative vs. Discriminative models_ The problem of overcounting evidence (12_15).mp412.22MB
8 - 6 - ximizing the Likelihood (10_29).mp49.83MB
9 - 1 - Introduction to Infortion Extraction (9_18).mp49.39MB
9 - 2 - Evaluation of Named Entity Recognition (6_34).mp46.75MB
9 - 3 - Sequence Models for Named Entity Recognition (15_05).mp414.15MB
9 - 4 - ximum Entropy Sequence Models (13_01).mp413.30MB
- CreateTime2018-11-27
- UpdateTime2018-11-27
- FileTotalCount102
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