Conference proceeding
Combining structural and citation-based evidence for text classification
Proceedings of the thirteenth ACM international conference on information and knowledge management, pp.162-163
CIKM '04
11/13/2004
DOI: 10.1145/1031171.1031204
Abstract
This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM Digital Library and the ACM Computing Classification System show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.
Details
- Title: Subtitle
- Combining structural and citation-based evidence for text classification
- Creators
- Baoping Zhang - Virginia TechMarcos Gonçalves - Virginia TechWeiguo Fan - Virginia TechYuxin Chen - Virginia TechEdward Fox - Virginia TechPável Calado - Universidade Federal de Minas GeraisMarco Cristo - Universidade Federal de Minas Gerais
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the thirteenth ACM international conference on information and knowledge management, pp.162-163
- Publisher
- ACM
- Series
- CIKM '04
- DOI
- 10.1145/1031171.1031204
- Language
- English
- Date published
- 11/13/2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380506902771
Metrics
4 Record Views