Abstract
Measuring text similarity has shown its fundamental utilization in various text mining application problems. This paper proposes a new method based on classification algorithms for measuring the similarity between two texts. Specifically, a sentence-term matrix that describes the frequency of terms that occur in a collection of sentences was created to measure the classification accuracy of two texts. Our idea is based on the fact that similar texts are difficult to distinguish from each other, which should lead to a low classification accuracy between similar texts. By doing comparative experiments on several widely used text similarity measures, analysis results with real data from the Machine Learning Repository at the University of California, Irvine demonstrate that the proposed method is able to achieve outperformed the other existing similarity measures across the entire range of term selection filters.
| Original language | English |
|---|---|
| Pages (from-to) | 328-339 |
| Number of pages | 12 |
| Journal | International Journal of Industrial Engineering : Theory Applications and Practice |
| Volume | 24 |
| Issue number | 3 |
| Publication status | Published - 2017 |
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Keywords
- Classification
- Sentence-term matrix
- Text mining
- Text similarity measure
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
A data-driven text similarity measure based on classification algorithms. / Cho, Su Gon; Kim, Seoung Bum.
In: International Journal of Industrial Engineering : Theory Applications and Practice, Vol. 24, No. 3, 2017, p. 328-339.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A data-driven text similarity measure based on classification algorithms
AU - Cho, Su Gon
AU - Kim, Seoung Bum
PY - 2017
Y1 - 2017
N2 - Measuring text similarity has shown its fundamental utilization in various text mining application problems. This paper proposes a new method based on classification algorithms for measuring the similarity between two texts. Specifically, a sentence-term matrix that describes the frequency of terms that occur in a collection of sentences was created to measure the classification accuracy of two texts. Our idea is based on the fact that similar texts are difficult to distinguish from each other, which should lead to a low classification accuracy between similar texts. By doing comparative experiments on several widely used text similarity measures, analysis results with real data from the Machine Learning Repository at the University of California, Irvine demonstrate that the proposed method is able to achieve outperformed the other existing similarity measures across the entire range of term selection filters.
AB - Measuring text similarity has shown its fundamental utilization in various text mining application problems. This paper proposes a new method based on classification algorithms for measuring the similarity between two texts. Specifically, a sentence-term matrix that describes the frequency of terms that occur in a collection of sentences was created to measure the classification accuracy of two texts. Our idea is based on the fact that similar texts are difficult to distinguish from each other, which should lead to a low classification accuracy between similar texts. By doing comparative experiments on several widely used text similarity measures, analysis results with real data from the Machine Learning Repository at the University of California, Irvine demonstrate that the proposed method is able to achieve outperformed the other existing similarity measures across the entire range of term selection filters.
KW - Classification
KW - Sentence-term matrix
KW - Text mining
KW - Text similarity measure
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M3 - Article
AN - SCOPUS:85032832225
VL - 24
SP - 328
EP - 339
JO - International Journal of Industrial Engineering : Theory Applications and Practice
JF - International Journal of Industrial Engineering : Theory Applications and Practice
SN - 1072-4761
IS - 3
ER -