research-article

Discovering relationships among categories using misclassification information

Authors:

Saket S. R. Mengle,

Nazli Goharian,

Alana PlattAuthors Info & Claims

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

Pages 932 - 937

https://doi.org/10.1145/1363686.1363899

Published: 16 March 2008 Publication History

Get Access

Abstract

Knowledge of relationships among categories is of the interest in different domains such as text classification, content analysis, and text mining. We propose and evaluate approaches to effectively identify relationships among document categories. Our proposed novel method capitalizes on the misclassification results of a text classifier to identify potential relationships among categories. We demonstrate that our system detects such relationships, even those relationships that assessors failed to identify in manual evaluation. Furthermore, we favorably compare the effectiveness of our methods with the state of art method and demonstrate a significant improvement in precision (34%) and recall (5%).

References

[1]

20 News Groups dataset. http://people.csail.mit.edu/jrennie/20Newsgroups

Google Scholar

[2]

Dhillon I., Mallela S. and Modha D., Information-Theoretic Co-Clustering. The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, 2003

Digital Library

Google Scholar

[3]

Gao B., Liu T., Cheng Q., Feng G., Qin T., and Ma W., Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Co-partitioning. IEEE Transactions on Knowledge and Data Engineering Volume 17 Issue 9, Special Issue on Data Preparation, 2005

Digital Library

Google Scholar

[4]

Mengle S, Goharian N and Platt A., FACT: Fast Algorithm for Categorizing Text. The 5th IEEE International Conference on Intelligence and Security Informatics, 2007

Google Scholar

[5]

Open Directory Project (http://dmoz.org)

Google Scholar

[6]

Punera K., Rajan S. and Ghosh J., Automatically Learning document taxonomy for Hierarchical classification. The 14th International Conference on World Wide Web, 2005

Digital Library

Google Scholar

[7]

Tan P. N., Stienbach M, and Kumar V, Introduction to Data Mining, Addison Wesley, 2006

Digital Library

Google Scholar

[8]

Toutanova K., Chen F., Popat K. and Hofmann T., Text classification in a hierarchical mixture model for small training sets. The 10th ACM International Conference on Information and Knowledge Management, 2001

Digital Library

Google Scholar

[9]

Vapnik Vladimir, Statistical learning theory. Wiley, 1998

Google Scholar

[10]

Vural V. and Dy J., A Hierarchical Method for Multi-Class Support Vector Machines. The 21st International Conference on Machine Learning, 2004.

Digital Library

Google Scholar

[11]

Yahoo Directories (http://dir.yahoo.com)

Google Scholar

[12]

Zhu S., Ji X, Xu W. and Gong Y., Multilabelled Classification Using Maximum Entropy Method. The 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005

Digital Library

Google Scholar

[13]

Ziegler C-N., Simon K. and Lausen G., Automatic computation of semantic proximity using taxonomy knowledge. The 15th ACM international Conference on Information and Knowledge Management, 2006

Digital Library

Google Scholar

Cited By

View all

Kittiphattanabawon NTheeramunkong TNantajeewarawat E(2014)Region-based association measures for ranking mined news relationsIntelligent Data Analysis10.5555/2639292.263929918:2(217-241)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2639292.2639299
Mengle SGoharian NShin SOssowski SSchumacher MPalakal MHung C(2010)Mining temporal relationships among categoriesProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774319(1107-1108)Online publication date: 22-Mar-2010
https://dl.acm.org/doi/10.1145/1774088.1774319
Kittiphattanabawon NTheeramunkong TNantajeewarawat E(2010)Exploration of document relation quality with consideration of term representation basis, term weighting and association measureProceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics10.1007/978-3-642-13601-6_15(126-139)Online publication date: 21-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13601-6_15
Show More Cited By

Index Terms

Discovering relationships among categories using misclassification information
1. Information systems
  1. Information retrieval

Recommendations

Detecting relationships among categories using text classification

Discovering relationships among concepts and categories is crucial in various information systems. The authors' objective was to discover such relationships among document categories. Traditionally, such relationships are represented in the form of a ...
Mining temporal relationships among categories
SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Temporal text mining deals with discovering temporal patterns in text over a period of time. A Theme Evolution Graph (TEG) is used to visualize when new themes are created and how they evolve with respect to time. TEG, however, does not represent ...
Clustering and classification of music by interval categories
MCM'11: Proceedings of the Third international conference on Mathematics and computation in music

We present a novel approach to clustering and classification of music, based on the concept of interval categories. Six interval categories exist, each with its own musical character. A piece of music can be represented by six numbers, reflecting the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

March 2008

2586 pages

ISBN:9781595937537

DOI:10.1145/1363686

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Hisham M. Haddad
Kennesaw State University

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC '08

Sponsor:

SIGAPP

SAC '08: The 2008 ACM Symposium on Applied Computing

March 16 - 20, 2008

Fortaleza, Ceara, Brazil

Acceptance Rates

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

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
175
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kittiphattanabawon NTheeramunkong TNantajeewarawat E(2014)Region-based association measures for ranking mined news relationsIntelligent Data Analysis10.5555/2639292.263929918:2(217-241)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2639292.2639299
Mengle SGoharian NShin SOssowski SSchumacher MPalakal MHung C(2010)Mining temporal relationships among categoriesProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774319(1107-1108)Online publication date: 22-Mar-2010
https://dl.acm.org/doi/10.1145/1774088.1774319
Kittiphattanabawon NTheeramunkong TNantajeewarawat E(2010)Exploration of document relation quality with consideration of term representation basis, term weighting and association measureProceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics10.1007/978-3-642-13601-6_15(126-139)Online publication date: 21-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13601-6_15
Mengle SGoharian N(2010)Detecting relationships among categories using text classificationJournal of the American Society for Information Science and Technology10.1002/asi.2129761:5(1046-1061)Online publication date: 2-Mar-2010
https://doi.org/10.1002/asi.21297
Platt AMengle SGoharian NShin SOssowski S(2009)Improving classification based off-topic search detection via category relationshipsProceedings of the 2009 ACM symposium on Applied Computing10.1145/1529282.1529469(869-874)Online publication date: 8-Mar-2009
https://dl.acm.org/doi/10.1145/1529282.1529469
Kittiphattanabawon NTheeramunkong T(2009)Relation Discovery from Thai News Articles Using Association Rule MiningProceedings of the Pacific Asia Workshop on Intelligence and Security Informatics10.1007/978-3-642-01393-5_13(118-129)Online publication date: 28-Apr-2009
https://dl.acm.org/doi/10.1007/978-3-642-01393-5_13

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Detecting relationships among categories using text classification

Mining temporal relationships among categories

Clustering and classification of music by interval categories