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Automatic zone identification in scientific papers via fusion techniques

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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.

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Authors and Affiliations

  1. IT Research Faculty, ICT Research Institute, Tehran, Iran

    Nasrin Asadi

  2. E-Services and E-Content Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran

    Kambiz Badie

  3. Data Processing and Analysis Systems Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran

    Maryam Tayefeh Mahmoudi

Authors
  1. Nasrin Asadi
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  2. Kambiz Badie
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  3. Maryam Tayefeh Mahmoudi
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Correspondence to Maryam Tayefeh Mahmoudi.

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Asadi, N., Badie, K. & Mahmoudi, M.T. Automatic zone identification in scientific papers via fusion techniques. Scientometrics 119, 845–862 (2019). https://doi.org/10.1007/s11192-019-03060-9

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