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Classifying Text in Citation Context as Relevant or Irrelevant to the Cited Paper

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  • Afsheen Khalid

    (Center for Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan)

Abstract

Citation contexts, whether in the form of full citing sentences or text within a fixed window around the citation, have been widely used in various citation analysis applications. However, the absence of precise techniques to identify the exact span of text describing citations forces these applications to rely on extended texts as citation contexts. In this paper, we introducednew features combined with baseline features to accurately identify text that characterizes citations. Specifically, we utilizeda Conditional Random Field (CRF)sequence classifier to categorize the surrounding text of citations as relevant or irrelevant. The integration of these features enhances the precision, recall, and F-measure scores for the Relevant (R) class. Although the average values of all measures are similar to those obtained with baseline features alone. Our approach significantly improves the extraction of relevant text.

Suggested Citation

  • Afsheen Khalid, 2024. "Classifying Text in Citation Context as Relevant or Irrelevant to the Cited Paper," International Journal of Innovations in Science & Technology, 50sea, vol. 6(3), pages 1088-1098, August.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:3:p:1088-1098
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    References listed on IDEAS

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    1. Clara Martinez-Perez & Cristina Alvarez-Peregrina & Cesar Villa-Collar & Miguel Ángel Sánchez-Tena, 2020. "Current State and Future Trends: A Citation Network Analysis of the Academic Performance Field," IJERPH, MDPI, vol. 17(15), pages 1-24, July.
    2. Shengbo Liu & Chaomei Chen & Kun Ding & Bo Wang & Kan Xu & Yuan Lin, 2014. "Literature retrieval based on citation context," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1293-1307, November.
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