0.25
0.5
0.75
1.25
1.5
1.75
2
Text Classification
Published on Feb 25, 200752940 Views
Related categories
Chapter list
Text Classification: An Advanced Tutorial00:00
Outline00:39
Text Classification: definition01:29
Text Classification: Examples02:08
Text Classification: Examples0103:41
Representing text for classification04:29
Representing text: a list of words04:52
Text Classification with Naive Bayes05:47
Text Classification with Naive Bayes0107:19
Text Classification with Naive Bayes0207:58
Text Classification with Naive Bayes0309:24
Naïve Bayes for SPAM filtering (Sahami et al, 1998)11:06
circa 2003…12:08
TITLE12:26
Naive Bayes Summary12:42
Outline13:48
Representing text: a list of words14:08
Representing text: a bag of words15:13
The Curse of Dimensionality16:51
Margin-based Learning20:09
The Voted Perceptron21:03
The Voted Perceptron: Proof25:37
Lessons of the Voted Perceptron25:50
More on Support Vectors for Text26:48
Support Vector Machine Results28:31
TF-IDF Representation29:44
TF-IDF Representation0131:06
Support Vector Machine Results32:20
TF-IDF Representation33:27
Other Fast Discriminative Methods34:16
Other Fast Discriminative Methods0135:48
Outline36:33
Text Classification: Examples36:50
Classifying Reviews as Favorable or Not37:37
Classifying Reviews as Favorable or Not0139:35
Classifying Reviews as Favorable or Not0239:58
Classifying Movie Reviews40:51
Classifying Movie Reviews0143:59
Classifying Movie Reviews0244:36
Classifying Movie Reviews0345:15
Classifying Movie Reviews0445:27
Classifying Movie Reviews0546:36
Classifying Movie Reviews0646:45
Classifying Movie Reviews0747:30
Classifying Movie Reviews0848:23
Outline48:47
Classifying Email into Acts 49:04
Idea: Predicting Acts from Surrounding Acts49:35
Evidence of Sequential Correlation of Acts50:23
Content versus Context50:45
Content versus Context0151:30
Collective Classification algorithm (based on Dependency Networks Model)51:54
Agreement versus Iteration53:03
Outline53:24
Summary & Conclusions53:37