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A novel word ranking method based on distorted entropy

Author

Listed:
  • 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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    1. 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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    5. Mehri, Ali & Darooneh, Amir H., 2011. "The role of entropy in word ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(18), pages 3157-3163.
    6. 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.
    7. Alejandro Balbás & José Garrido & Silvia Mayoral, 2009. "Properties of Distortion Risk Measures," Methodology and Computing in Applied Probability, Springer, vol. 11(3), pages 385-399, September.
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    Cited by:

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