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Fusion Matrix–Based Text Similarity Measures for Clustering of Retrieval Results

Author

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  • Yueyang Zhao

    (Shengjing Hospital of China Medical University)

  • Lei Cui

    (China Medical University)

Abstract

To address the deficiency in semantic representations of medical texts and achieve the clustering of PubMed database retrieval results, this study presented a method to construct a fusion matrix using text similarity measures. Similarity relations between phrases, texts, and the content of phrases and texts were combined to create a fusion matrix, and several clustering algorithms were trained to group a collection of texts from the PubMed database. Category annotations were then created to describe the meaning of each category of clustered texts. Experimental results showed that the fusion matrix-based clustering was superior in grouping the text sets, and clustering the training set was not necessary to improve clustering performance. Moreover, the extracted high-frequency words in the category descriptions distinguished the meanings of the categories well; therefore, the fusion matrix design was effective for clustering descriptions of academic texts. As only the PubMed database was used in this study, future research should extend the fusion matrix to other text repositories.

Suggested Citation

  • Yueyang Zhao & Lei Cui, 2023. "Fusion Matrix–Based Text Similarity Measures for Clustering of Retrieval Results," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1163-1186, February.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:2:d:10.1007_s11192-022-04596-z
    DOI: 10.1007/s11192-022-04596-z
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    References listed on IDEAS

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