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A novel word ranking method based on distorted entropy

Author

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  • Mehri, Ali
  • Agahi, Hamzeh
  • Mehri-Dehnavi, Hossein

Abstract

This paper proposes an application of distorted entropy as well-known tools for non-additive expected utility theory in word ranking. Our algorithms for two books “Statistical Inference” by Casella and Berger and “The Origin of Species” by Charles Darwin show that our method on the distorted entropy improves the corresponding ones in the literature.

Suggested Citation

  • Mehri, Ali & Agahi, Hamzeh & Mehri-Dehnavi, Hossein, 2019. "A novel word ranking method based on distorted entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 484-492.
    • Handle: RePEc:eee:phsmap:v:521:y:2019:i:c:p:484-492
    DOI: 10.1016/j.physa.2019.01.080
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    References listed on IDEAS

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    7. Jamaati, Maryam & Mehri, Ali, 2018. "Text mining by Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1368-1376.
    8. Yang, Zhen & Lei, Jianjun & Fan, Kefeng & Lai, Yingxu, 2013. "Keyword extraction by entropy difference between the intrinsic and extrinsic mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4523-4531.
    9. Carretero-Campos, C. & Bernaola-Galván, P. & Coronado, A.V. & Carpena, P., 2013. "Improving statistical keyword detection in short texts: Entropic and clustering approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1481-1492.
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