Difference between revisions of "Melia et al AISTATS 2001"

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(Created page with '== Citation == Marina Melia and Jianbo Shi. 2001. A Random Walks View of Spectral Segmentation. In AISTATS 2001. == Online version == [http://www.stat.washington.edu/mmp/Paper…')
 
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== Summary ==
 
== Summary ==
  
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This is an early and influential [[Category::paper]] that introduced the use of supervised learning for [[AddressesProblem::review classification]].  The authors used a corpus of [[UsesDataset::Pang Movie Reviews|1400 movie reviews]] that had been rated (by the authors) as positive or negative, and compared several approaches to predicting polarity.
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This paper gives a general theoretical interpretation of a wide variety of spectral methods.  The authors first present the general framework of normalized cuts.  Begin with an index set <math>I</math>
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# A hand-coded lexicon of polar words.  This gave accuracy of around 70% (the dataset is balanced so random chance would be 50%).
 
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# Off-the-shelf classifier learners ([[UsesMethod::Naive Bayes classifier learning|Naive Bayes]] and [[UsesMethod::Support vector machine classifier learning|SVM-lite]]) which performed well on topical text classification.  This gave accuracy in the high 70's and low 80's.
 
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Some feature-engineering techniques suggested by the results in [[RelatedPaper::Turney, ACL 2002]] were explored, such as using phrases (bigrams) instead of unigrams, and using part of speech information, without major improvements in accuracy.
 
  
 
== Related papers ==
 
== Related papers ==

Revision as of 13:51, 4 February 2011

Citation

Marina Melia and Jianbo Shi. 2001. A Random Walks View of Spectral Segmentation. In AISTATS 2001.

Online version

Available on Marina Melia's Website

Summary

This paper gives a general theoretical interpretation of a wide variety of spectral methods. The authors first present the general framework of normalized cuts. Begin with an index set

Related papers

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