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8 - 2 - Making features from text for discriminative NLP models (18-11)06.04.2012181
8 - 3 - Feature-Based Linear Classifiers (13-34)06.04.2012161
8 - 5 - Generative vs. Discriminative models- The problem of overcounting evidence (12-15)06.04.2012172
8 - 6 - Maximizing the Likelihood (10-29)06.04.2012184
8 - 4 - Building a Maxent Model- The Nuts and Bolts (8-04)06.04.2012154
8 - 1 - Generative vs. Discriminative Models (7-49)06.04.2012143
7 - 4 - Learning Sentiment Lexicons (14_45)06.04.2012149
7 - 2 - Sentiment Analysis_ A baseline algorithm (13_27)06.04.2012139
7 - 5 - Other Sentiment Tasks (11_01)06.04.2012139
7 - 3 - Sentiment Lexicons (8_37)06.04.2012154
7 - 1 - What is Sentiment Analysis- (7-17)06.04.2012131
6 - 7 - Precision, Recall, and the F measure (16_16)05.04.2012123
6 - 8 - Text Classification_ Evaluation (7_17)05.04.2012141
6 - 9 - Practical Issues in Text Classification (5_56)05.04.2012123
6 - 6 - Multinomial Naive Bayes_ A Worked Example (8_58)05.04.2012131
5 - 2 - The Noisy Channel Model of Spelling (19_30)05.04.2012128
6 - 4 - Naive Bayes_ Learning (5_22)05.04.2012138
6 - 3 - Formalizing the Naive Bayes Classifier (9_28)05.04.2012120
6 - 5 - Naive Bayes_ Relationship to Language Modeling (4_35)05.04.2012129
6 - 1 - What is Text Classification- (8-12).mp405.04.2012131
4 - 7 - Good-Turing Smoothing (15_35)05.04.2012116
6 - 2 - Naive Bayes (3_19)05.04.2012114
5 - 3 - Real-Word Spelling Correction (9_19)05.04.2012111
5 - 4 - State of the Art Systems (7_10)05.04.2012111
