A data-driven text similarity measure based on classification algorithms

Su Gon Cho, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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 languageEnglish
Pages (from-to)328-339
Number of pages12
JournalInternational Journal of Industrial Engineering : Theory Applications and Practice
Volume24
Issue number3
Publication statusPublished - 2017

Bibliographical note

Funding Information:
The authors would like to thank the editor and reviewers for their useful comments and suggestions, which greatly helped in improving the quality of the paper. This research was supported by Brain Korea PLUS, Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (NRF-2016R1A2B1008994), and the Ministry of Trade, Industry & Energy under Industrial Technology Innovation Program (R1623371).

Publisher Copyright:
© International Journal of Industrial Engineering.

Keywords

  • Classification
  • Sentence-term matrix
  • Text mining
  • Text similarity measure

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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