Abstract
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 language | English |
|---|---|
| Title of host publication | ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference |
| Editors | Mark Reynold |
| Place of Publication | Darlinghurst, Australia |
| Publisher | Australian Computer Society |
| Pages | 145-152 |
| Number of pages | 7 |
| Volume | 113 |
| ISBN (Print) | 9781920682934 |
| Publication status | Published - 17 Jan 2011 |
| Event | 34th Australasian Computer Science Conference (ACSC 2011) - Perth, Perth, Australia Duration: 17 Jan 2011 → 20 Jan 2011 https://50years.acs.org.au/content/dam/acs/50-years/journals/crpit/Vol113.pdf |
Conference
| Conference | 34th Australasian Computer Science Conference (ACSC 2011) |
|---|---|
| Abbreviated title | ACSC 2011 |
| Country | Australia |
| City | Perth |
| Period | 17/01/11 → 20/01/11 |
| Internet address |
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A new term ranking method based on relation extraction and graph model for text classification. / Hyunh, Dat; Tran, Dat; Ma, Wanli; Sharma, Dharmendra.
ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference. ed. / Mark Reynold. Vol. 113 Darlinghurst, Australia : Australian Computer Society, 2011. p. 145-152.Research output: A Conference proceeding or a Chapter in Book › Conference contribution
TY - GEN
T1 - A new term ranking method based on relation extraction and graph model for text classification
AU - Hyunh, Dat
AU - Tran, Dat
AU - Ma, Wanli
AU - Sharma, Dharmendra
PY - 2011/1/17
Y1 - 2011/1/17
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Text Classification
M3 - Conference contribution
SN - 9781920682934
VL - 113
SP - 145
EP - 152
BT - ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference
A2 - Reynold, Mark
PB - Australian Computer Society
CY - Darlinghurst, Australia
ER -