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Concept symbols revisited: Naming clusters by parsing and filtering of noun phrases from citation contexts of concept symbols

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  • Jesper W. Schneider

    (Royal School of Library and Information Science, Department of Information Studies)

Abstract

Summary The present study presents a semi-automatic method for parsing and filtering of noun phrases from citation contexts of concept symbols. The purpose of the method is to extract contextual, agreed upon, and pertinent noun phrases, to be used in visualization studies for naming clusters (concept groups) or concept symbols. The method is applied in a case study, which forms part of a larger dissertation work concerning the applicability of bibliometric methods for thesaurus construction. The case study is carried out within periodontology, a specialty area of dentistry. The result of the case study indicates that the method is able to identify highly important noun phrases, and that these phrases accurately describe their parent clusters. Hence, the method is able to reduce the labour intensive work of manual citation context analysis, though further refinements are still needed.

Suggested Citation

  • Jesper W. Schneider, 2006. "Concept symbols revisited: Naming clusters by parsing and filtering of noun phrases from citation contexts of concept symbols," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 573-593, September.
    • Handle: RePEc:spr:scient:v:68:y:2006:i:3:d:10.1007_s11192-006-0131-z
    DOI: 10.1007/s11192-006-0131-z
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    Citations

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    Cited by:

    1. Sumita Raghuram & Philipp Tuertscher & Raghu Garud, 2010. "Research Note ---Mapping the Field of Virtual Work: A Cocitation Analysis," Information Systems Research, INFORMS, vol. 21(4), pages 983-999, December.
    2. Marc Bertin & Iana Atanassova & Cassidy R. Sugimoto & Vincent Lariviere, 2016. "The linguistic patterns and rhetorical structure of citation context: an approach using n-grams," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1417-1434, December.
    3. Henry Small, 2010. "Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 835-849, June.
    4. Nees Jan Eck & Ludo Waltman & Ed C. M. Noyons & Reindert K. Buter, 2010. "Automatic term identification for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 581-596, March.
    5. Henry Small, 2011. "Interpreting maps of science using citation context sentiments: a preliminary investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 373-388, May.
    6. Carlos Olmeda-Gómez & Maria-Antonia Ovalle-Perandones & Antonio Perianes-Rodríguez, 2017. "Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 195-217, October.
    7. Horbach, Serge & Aagaard, Kaare & Schneider, Jesper W., 2021. "Meta-Research: How problematic citing practices distort science," MetaArXiv aqyhg, Center for Open Science.
    8. van Eck, N.J.P. & Waltman, L. & Noyons, E.C.M. & Buter, R.K., 2008. "Automatic Term Identification for Bibliometric Mapping," ERIM Report Series Research in Management ERS-2008-081-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Katherine W. McCain, 2012. "Assessing Obliteration by Incorporation: Issues and Caveats," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(11), pages 2129-2139, November.

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