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Comparing discrimination powers of text and citation-based context types

Author

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  • Hajar Sotudeh

    (Shiraz University)

  • Mojgan Houshyar

    (Shiraz University)

Abstract

Different context types have different potentialities in representing subjects of documents. However, little is known on their probable differences regarding discriminating documents with different subjects. The present study aimed to compare the discrimination powers of five context types (i.e. title, textual, text-citation, reference, and reference-title contexts). Using a content analysis method, a test collection consisted of 3637 papers containing 15 English homographs in 54 subject clusters were analyzed by discriminant analysis to determine the discrimination among the clusters. The results confirmed that the homograph context types significantly discriminate between the clusters. The contexts can correctly cluster the documents from about 40 to 80%, with text and reference-title context types being stronger in differentiating the clusters. This is the first study to compare the powers of different textual contexts kinds and recommend to concentrate on context types to discriminate between documents with homographs to improve the effectiveness of their retrieval. It is suggested that a comparison of the context types be used as a tool in re-ranking or clustering of the retrieved results.

Suggested Citation

  • Hajar Sotudeh & Mojgan Houshyar, 2018. "Comparing discrimination powers of text and citation-based context types," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 229-251, January.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:1:d:10.1007_s11192-017-2566-9
    DOI: 10.1007/s11192-017-2566-9
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    References listed on IDEAS

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    1. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    2. Dietmar Wolfram, 2015. "The symbiotic relationship between information retrieval and informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2201-2214, March.
    3. 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.
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