Enhanced intelligent text categorization using concise keyword analysis

Shahi, Amir Mohammad, Issac, Biju and Modapothala, Jashua Rajesh (2012) Enhanced intelligent text categorization using concise keyword analysis. In: ICIMTR 2012 - 2012 International Conference on Innovation, Management and Technology Research, 21st - 22nd May 2012, Malacca, Malaysia.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/ICIMTR.2012.6236461


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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Categorization, Corporate Sustainability Report, Feature Selection, Global Reporting Initiative, Machine Learning, Supervised Learning, Text Ontology
Subjects: G400 Computer Science
X300 Academic studies in Education
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 17 Dec 2018 16:44
Last Modified: 11 Oct 2019 15:02
URI: http://nrl.northumbria.ac.uk/id/eprint/37306

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