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Machine Learning over Text & Images - Autumn School

Text Classification

author: William Cohen, Carnegie Mellon University
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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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Reviews and comments:

Comment1 Saad, January 20, 2008 at 11:38 a.m.:

Beautiful lecture.feel lucky to hear sir william

Comment2 Amanjit Singh Gill, March 4, 2008 at 10:14 a.m.:

Rock and roll...

Comment3 Nady Gomes, May 17, 2008 at 9:04 p.m.:

I am thinking to choose this topic for my project next year and I found this information really interesting.

Thank you

Comment4 Muhammad Rafi, May 30, 2008 at 9:28 a.m.:

Great Teaching ... best regards, R/\Fi

Comment5 shweta, June 6, 2008 at 8:03 a.m.:

sir i have a project in text classification. in my project i have to develop srls algo ,plz tell me how can i develop this algo?
thank you

Comment6 koushik, July 9, 2008 at 9:23 a.m.:

i am also doing work in text classification , my mail id koushikbuie@yahoo.co.in . contact wit me

Comment7 koushik, October 3, 2008 at 12:52 p.m.:

i am trying to reduce feature values. what are the different methods will be most efficient , TF_IDF,or any o0ther e4lse /
what is the importance of Latent semantic INdexing.

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