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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/232909
- Title
- Enhanced intelligent text categorization using concise keyword analysis
- Author(s)
- Shahi, Amir Mohammad; Issac, Biju; Modapothala, Jashua Rajesh
- Abstract
- Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many text classification problems. In this research, we explored the opportunities of addressing this pitfall by studying the ontological characteristics of document categories and grouping them under virtual super-categories to narrow down the search for a suitable category. Applying this method showed that classifier performance has greatly improved despite the relatively small size of the training corpus.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Sarawak Campus. School of Engineering, Computing and Science
- Research centre
- Swinburne University of Technology. Sarawak Campus. School of Business and Design
- Source
- Proceedings of the 2012 International Conference on Innovation, Management and Technology Research (ICIMTR 2012), Malacca, Malaysia, 21-22 May 2012, pp. 574-579
- Publication year
- 2012
- Keyword(s)
- Corporate Sustainability Reports; Document categorization; Feature selection; Global Reporting Initiatives; Machine learning; Supervised learning; Text categorization; Text ontology
- Publisher
- IEEE
- ISBN
- 9781467306553, 146730655X
- Publisher URL
- http://dx.doi.org/10.1109/ICIMTR.2012.6236461
- Copyright
- Copyright © 2012 IEEE.
- Peer reviewed
