Article

SAT-MOD: moderate itemset fittest for text classification

Authors:

Jianlin Feng,

Huijun Liu,

Jing ZouAuthors Info & Claims

WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web

Pages 1054 - 1055

https://doi.org/10.1145/1062745.1062865

Published: 10 May 2005 Publication History

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Abstract

In this paper, we present a novel association-based method called SAT-MOD for text classification. SAT-MOD views a sentence rather than a document as a transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. The effectiveness of SAT-MOD has been demonstrated comparable to well-known alternatives such as LinearSVM and much better than current document-level words association based methods on the Reuters corpus.

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Figueiredo FRocha LCouto TSalles TGonçalves MMeira Jr. W(2011)Word co-occurrence features for text classificationInformation Systems10.1016/j.is.2011.02.00236:5(843-858)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.is.2011.02.002
Qian TXiong HWang YChen E(2007)On the strength of hyperclique patterns for text categorizationInformation Sciences: an International Journal10.1016/j.ins.2007.04.005177:19(4040-4058)Online publication date: 1-Oct-2007
https://dl.acm.org/doi/10.1016/j.ins.2007.04.005
Qian TXiong HWang YChen EYu PTsotras VFox ELiu B(2006)Adapting association patterns for text categorizationProceedings of the 15th ACM international conference on Information and knowledge management10.1145/1183614.1183728(782-783)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1145/1183614.1183728
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Index Terms

SAT-MOD: moderate itemset fittest for text classification
1. Applied computing
  1. Document management and text processing

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Published In

WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web

May 2005

454 pages

ISBN:1595930515

DOI:10.1145/1062745

Conference Chairs:
Allan Ellis
Southern Cross University, Australia
,
Tatsuya Hagino
Keio University, Japan
,
Program Chairs:
Fred Douglis
IBM Research
,
Prabhakar Raghavan
Verity, Inc.

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

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Published: 10 May 2005

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Figueiredo FRocha LCouto TSalles TGonçalves MMeira Jr. W(2011)Word co-occurrence features for text classificationInformation Systems10.1016/j.is.2011.02.00236:5(843-858)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.is.2011.02.002
Qian TXiong HWang YChen E(2007)On the strength of hyperclique patterns for text categorizationInformation Sciences: an International Journal10.1016/j.ins.2007.04.005177:19(4040-4058)Online publication date: 1-Oct-2007
https://dl.acm.org/doi/10.1016/j.ins.2007.04.005
Qian TXiong HWang YChen EYu PTsotras VFox ELiu B(2006)Adapting association patterns for text categorizationProceedings of the 15th ACM international conference on Information and knowledge management10.1145/1183614.1183728(782-783)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1145/1183614.1183728
Qian TWang YXiang LGong W(2006)Feature selection, rule extraction, and score modelProceedings of the First international conference on Rough Sets and Knowledge Technology10.1007/11795131_69(476-481)Online publication date: 24-Jul-2006
https://dl.acm.org/doi/10.1007/11795131_69
Feng JLiu HFeng Y(2005)Sentential association based text classification systemsProceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development10.1007/978-3-540-31849-1_101(1037-1040)Online publication date: 29-Mar-2005
https://dl.acm.org/doi/10.1007/978-3-540-31849-1_101

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Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values

Using some web content mining techniques for Arabic text classification

Text classification using sentential frequent itemsets

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