A new term ranking method based on relation extraction and graph model for text classification

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Abstract

Term frequency and document frequency are currently used to measure term significance in text classification. However, these measures cannot provide sufficient information to differentiate important terms. Thus, in this research, a new term ranking (weighting) approach for text classification will be proposed.
The approach firstly is based on relations among terms to estimates the important levels of terms in a document. Secondly, the proposed approach provides a considerable representation for the text documents. The results from experiment show that with the same data in Wikipedia corpus the term weighting approach provides higher accuracy in comparison to the popular approaches based on term frequency.
Original languageEnglish
Title of host publicationASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference
EditorsMark Reynold
Place of PublicationDarlinghurst, Australia
PublisherAustralian Computer Society
Pages145-152
Number of pages7
Volume113
ISBN (Print)9781920682934
Publication statusPublished - 17 Jan 2011
Event34th Australasian Computer Science Conference (ACSC 2011) - Perth, Perth, Australia
Duration: 17 Jan 201120 Jan 2011
https://50years.acs.org.au/content/dam/acs/50-years/journals/crpit/Vol113.pdf

Conference

Conference34th Australasian Computer Science Conference (ACSC 2011)
Abbreviated titleACSC 2011
CountryAustralia
CityPerth
Period17/01/1120/01/11
Internet address

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Cite this

Hyunh, D., Tran, D., Ma, W., & Sharma, D. (2011). A new term ranking method based on relation extraction and graph model for text classification. In M. Reynold (Ed.), ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference (Vol. 113, pp. 145-152). Darlinghurst, Australia: Australian Computer Society.