Machine Learning over Text & Images - Autumn School

Text Classification

author: William Cohen, Carnegie Mellon University

Categories

Top: Computer Science: Text Mining

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*Slides*
0:00	Text Classification: An Advanced Tutorial
0:39	Outline
1:29	Text Classification: definition
2:08	Text Classification: Examples
3:41	Text Classification: Examples01
4:29	Representing text for classification
4:52	Representing text: a list of words
5:47	Text Classification with Naive Bayes
7:19	Text Classification with Naive Bayes01
7:58	Text Classification with Naive Bayes02
9:24	Text Classification with Naive Bayes03
11:06	Naïve Bayes for SPAM filtering (Sahami et al, 1998)
12:08	circa 2003…
12:26	TITLE
12:42	Naive Bayes Summary
13:48	Outline
14:08	Representing text: a list of words
15:13	Representing text: a bag of words
16:51	The Curse of Dimensionality
20:09	Margin-based Learning
21:03	The Voted Perceptron
25:37	The Voted Perceptron: Proof
25:50	Lessons of the Voted Perceptron
26:48	More on Support Vectors for Text
28:31	Support Vector Machine Results
29:44	TF-IDF Representation
31:06	TF-IDF Representation01
32:20	Support Vector Machine Results
33:27	TF-IDF Representation
34:16	Other Fast Discriminative Methods
35:48	Other Fast Discriminative Methods01
36:33	Outline
36:50	Text Classification: Examples
37:37	Classifying Reviews as Favorable or Not
39:35	Classifying Reviews as Favorable or Not01
39:58	Classifying Reviews as Favorable or Not02
40:51	Classifying Movie Reviews
43:59	Classifying Movie Reviews01
44:36	Classifying Movie Reviews02
45:15	Classifying Movie Reviews03
45:27	Classifying Movie Reviews04
46:36	Classifying Movie Reviews05
46:45	Classifying Movie Reviews06
47:30	Classifying Movie Reviews07
48:23	Classifying Movie Reviews08
48:47	Outline
49:04	Classifying Email into Acts
49:35	Idea: Predicting Acts from Surrounding Acts
50:23	Evidence of Sequential Correlation of Acts
50:45	Content versus Context
51:30	Content versus Context01
51:54	Collective Classification algorithm (based on Dependency Networks Model)
53:03	Agreement versus Iteration
53:24	Outline
53:37	Summary & Conclusions

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