Improving the accuracy of co‐citation clustering using full text

Kevin W. Boyack, Henry Small and Richard Klavans

Journal of the American Society for Information Science and Technology, 2013, vol. 64, issue 9, 1759-1767

Abstract: Historically, co‐citation models have been based only on bibliographic information. Full‐text analysis offers the opportunity to significantly improve the quality of the signals upon which these co‐citation models are based. In this work we study the effect of reference proximity on the accuracy of co‐citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co‐citation clustering using only the bibliographic information to results from co‐citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co‐citation cluster solution by up to 30% over the traditional approach based on bibliographic information.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://doi.org/10.1002/asi.22896

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:64:y:2013:i:9:p:1759-1767

Ordering information: This journal article can be ordered from
https://doi.org/10.1002/(ISSN)1532-2890

Access Statistics for this article

More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology
Bibliographic data for series maintained by Wiley Content Delivery ().