Enhanced intelligent text categorization using concise keyword analysis

Hdl Handle:
http://hdl.handle.net/10149/249795
Title:
Enhanced intelligent text categorization using concise keyword analysis
Book Title:
Proceedings of IEEE International Conference on Innovation, Management and Technology Research 2012 (ICIMTR 2012)
Authors:
Shahi, A. M. (Amir); Issac, B. (Biju); Modapothala, J. R. (Jashua)
Affiliation:
Swinburne University of Technology
Citation:
Shahi, A.M., Issac, B. and Modapothala, J.R. (2012) 'Enhanced intelligent text categorization using concise keyword analysis', Proceedings of IEEE International Conference on Innovation, Management and Technology Research 2012 (ICIMTR 2012), pp.574-579.
Publisher:
IEEE
Conference:
International Conference on Innovation, Management and Technology Research 2012 (ICIMTR 2012), Malacca, Malaysia, 21-22 May 2012.
Issue Date:
21-May-2012
URI:
http://hdl.handle.net/10149/249795
DOI:
10.1109/ICIMTR.2012.6236461
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6236461
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.
Type:
Meetings and Proceedings
Language:
en
Keywords:
machine learning; text ontology; categorization; corporate sustainability report; global reporting initiative
ISBN:
9781467306539; 9781467306546

Full metadata record

DC FieldValue Language
dc.contributor.authorShahi, A. M. (Amir)en_GB
dc.contributor.authorIssac, B. (Biju)en_GB
dc.contributor.authorModapothala, J. R. (Jashua)en_GB
dc.date.accessioned2012-10-22T16:04:12Zen
dc.date.available2012-10-22T16:04:12Zen
dc.date.issued2012-05-21en
dc.identifier.isbn9781467306539en
dc.identifier.isbn9781467306546en
dc.identifier.doi10.1109/ICIMTR.2012.6236461en
dc.identifier.urihttp://hdl.handle.net/10149/249795en
dc.description.abstractSupervised 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.en_GB
dc.language.isoenen
dc.publisherIEEEen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6236461en_GB
dc.subjectmachine learningen_GB
dc.subjecttext ontologyen_GB
dc.subjectcategorizationen_GB
dc.subjectcorporate sustainability reporten_GB
dc.subjectglobal reporting initiativeen_GB
dc.titleEnhanced intelligent text categorization using concise keyword analysisen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentSwinburne University of Technologyen_GB
dc.title.bookProceedings of IEEE International Conference on Innovation, Management and Technology Research 2012 (ICIMTR 2012)en_GB
dc.identifier.conferenceInternational Conference on Innovation, Management and Technology Research 2012 (ICIMTR 2012), Malacca, Malaysia, 21-22 May 2012.en_GB
or.citation.harvardShahi, A.M., Issac, B. and Modapothala, J.R. (2012) 'Enhanced intelligent text categorization using concise keyword analysis', Proceedings of IEEE International Conference on Innovation, Management and Technology Research 2012 (ICIMTR 2012), pp.574-579.en_GB
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