Article

Associative text categorization exploiting negated words

Authors:

Paolo GarzaAuthors Info & Claims

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

Pages 530 - 535

https://doi.org/10.1145/1141277.1141402

Published: 23 April 2006 Publication History

Abstract

Associative classification has been recently applied to text document categorization. However, differently from classification of structured data, the quality of the generated classifier is rather low. This effect is mainly due to the poor precision of generated rules.To increase the precision of associative classifiers we propose the use of classification rules including negated words, i.e. words that the considered document should not contain. Rules are in the form "If a document includes words A and B, but not word Z, then it belongs to class C₁". Mining classification rules with negated words becomes quickly intractable when decreasing the support threshold. We tackle this problem by means of an opportunistic approach, where negated words are only generated to specialize rules that may wrongly classify training documents. Hence precision is increased, without losing recall.Experiments on the Reuters corpus show that our classifier based on negated words achieves good precision and recall results, while yielding an easily interpretable model typical of associative classifiers.

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Cited By

Policicchio VPietramala ARullo P(2012)GAMoNArtificial Intelligence10.1016/j.artint.2012.07.003191-192(61-95)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1016/j.artint.2012.07.003
Pietramala APolicicchio VRullo P(2012)Automatic Filtering of Valuable Features for Text CategorizationAdvanced Data Mining and Applications10.1007/978-3-642-35527-1_24(284-295)Online publication date: 2012
https://doi.org/10.1007/978-3-642-35527-1_24
Chua SCoenen FMalcolm G(2010)Classification Inductive Rule Learning with Negated FeaturesAdvanced Data Mining and Applications10.1007/978-3-642-17316-5_12(125-136)Online publication date: 2010
https://doi.org/10.1007/978-3-642-17316-5_12
Show More Cited By

Index Terms

Associative text categorization exploiting negated words
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

April 2006

1967 pages

ISBN:1595931082

DOI:10.1145/1141277

Conference Chair:
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 April 2006

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SAC06: The 2006 ACM Symposium on Applied Computing

April 23 - 27, 2006

Dijon, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Policicchio VPietramala ARullo P(2012)GAMoNArtificial Intelligence10.1016/j.artint.2012.07.003191-192(61-95)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1016/j.artint.2012.07.003
Pietramala APolicicchio VRullo P(2012)Automatic Filtering of Valuable Features for Text CategorizationAdvanced Data Mining and Applications10.1007/978-3-642-35527-1_24(284-295)Online publication date: 2012
https://doi.org/10.1007/978-3-642-35527-1_24
Chua SCoenen FMalcolm G(2010)Classification Inductive Rule Learning with Negated FeaturesAdvanced Data Mining and Applications10.1007/978-3-642-17316-5_12(125-136)Online publication date: 2010
https://doi.org/10.1007/978-3-642-17316-5_12
Rioult FZanuttini BCrémilleux B(2010)Nonredundant Generalized Rules and Their Impact in ClassificationAdvances in Intelligent Information Systems10.1007/978-3-642-05183-8_1(3-25)Online publication date: 2010
https://doi.org/10.1007/978-3-642-05183-8_1
Rullo PPolicicchio VCumbo CIiritano S(2009)OlexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2008.20621:8(1118-1132)Online publication date: 1-Aug-2009
https://dl.acm.org/doi/10.1109/TKDE.2008.206
Rullo PCumbo CPolicicchio VCho YWainwright RHaddad HShin SKoo Y(2007)Learning rules with negation for text categorizationProceedings of the 2007 ACM symposium on Applied computing10.1145/1244002.1244098(409-416)Online publication date: 11-Mar-2007
https://dl.acm.org/doi/10.1145/1244002.1244098

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