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Automatic zone identification in scientific papers via fusion techniques

Author

Listed:
  • Nasrin Asadi

    (ICT Research Institute)

  • Kambiz Badie

    (ICT Research Institute)

  • Maryam Tayefeh Mahmoudi

    (ICT Research Institute)

Abstract

Zone identification is a topic in the area of text mining which helps researchers be benefited by the content of scientific papers in a satisfactory manner. The major aim of zone identification is to classify the sentences of scientific texts into some predefined zone categories which can be useful for summarization as well as information extraction. In this paper, we propose a two-level approach to zone identification within which the first level is in charge of classifying the sentences in a given paper based on some semantic and lexical features. In this respect, several machine learning algorithms such as Simple Logistics, Logistic Model Trees and Sequential Minimal Optimization are applied. The second level is responsible for applying fusion to the classification results obtained for consecutive sentences of the first level in order to make the final decision. The proposed method is evaluated on ART and DRI corpora as two well-known data sets. Results obtained for the accuracy of zone identification for these corpora are respectively 65.75% and 84.15%, which seem to be quite promising compared to those obtained by previous approaches.

Suggested Citation

  • Nasrin Asadi & Kambiz Badie & Maryam Tayefeh Mahmoudi, 2019. "Automatic zone identification in scientific papers via fusion techniques," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 845-862, May.
    • Handle: RePEc:spr:scient:v:119:y:2019:i:2:d:10.1007_s11192-019-03060-9
    DOI: 10.1007/s11192-019-03060-9
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    References listed on IDEAS

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    1. Kevin Heffernan & Simone Teufel, 2018. "Identifying problems and solutions in scientific text," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1367-1382, August.
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    Cited by:

    1. Bowen Ma & Chengzhi Zhang & Yuzhuo Wang & Sanhong Deng, 2022. "Enhancing identification of structure function of academic articles using contextual information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 885-925, February.

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