IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/1635708.html
   My bibliography  Save this article

Sentence Similarity Calculation Based on Probabilistic Tolerance Rough Sets

Author

Listed:
  • Ruiteng Yan
  • Dong Qiu
  • Haihuan Jiang

Abstract

Sentence similarity calculation is one of the important foundations of natural language processing. The existing sentence similarity calculation measurements are based on either shallow semantics with the limitation of inadequately capturing latent semantics information or deep learning algorithms with the limitation of supervision. In this paper, we improve the traditional tolerance rough set model, with the advantages of lower time complexity and becoming incremental compared to the traditional one. And then we propose a sentence similarity computation model from the perspective of uncertainty of text data based on the probabilistic tolerance rough set model. It has the ability of mining latent semantics information and is unsupervised. Experiments on SICK2014 task and STSbenchmark dataset to calculate sentence similarity identify a significant and efficient performance of our model.

Suggested Citation

  • Ruiteng Yan & Dong Qiu & Haihuan Jiang, 2021. "Sentence Similarity Calculation Based on Probabilistic Tolerance Rough Sets," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, January.
    • Handle: RePEc:hin:jnlmpe:1635708
    DOI: 10.1155/2021/1635708
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/1635708.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/1635708.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/1635708?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:1635708. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.