A DATA-DRIVEN DOCUMENT SIMILARITY MEASURE BASED ON CLASSIFICATION ALGORITHMS - Cho, Su Gon; Kim, Seoung Bum (International Journal of Industrial Engineering: Theory, Applications and Practice, 2017)
Measuring document similarity has shown its fundamental utilization in various text mining application problems. This paper propose 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 making comparative experiments on several widely used document 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.