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Identifying named entities in academic biographies with supervised learning

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  • Patrick Kenekayoro

    (Niger Delta University)

Abstract

Personal webpages of researchers or faculty members make up a percentage of the academic web. These webpages contain semi-structured or plain text information, and research has shown the importance of combining information extracted from multiple academic websites to create a unified database that can help in expert finding, and thus improve information retrieval for end users. This research identifies the kind of named entities that could be present in academic biographies by manually examining the biographies extracted from ORCID public profiles, and describes a method that uses natural language processing techniques and supervised machine learning to automatically extract these named entities from the plain text biographies. Up to 86% accuracy was achieved with support vector machines, demonstrating that the method used in this research can be suitable for creating a reusable trained model that extracts useful academic information from researchers’ personal profiles in webpages or other data sources.

Suggested Citation

  • Patrick Kenekayoro, 2018. "Identifying named entities in academic biographies with supervised learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 751-765, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2797-4
    DOI: 10.1007/s11192-018-2797-4
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Patrick Kenekayoro & Kevan Buckley & Mike Thelwall, 2014. "Automatic classification of academic web page types," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1015-1026, November.
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    Cited by:

    1. Arash Hajikhani & Arho Suominen, 2022. "Mapping the sustainable development goals (SDGs) in science, technology and innovation: application of machine learning in SDG-oriented artefact detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6661-6693, November.

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    More about this item

    Keywords

    Named entity recognition; Supervised learning; Natural language processing; Support vector machines; Random forests; Conditional random fields;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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