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

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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.

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Funding

This study was funded by the Liaoning Social Science Planning Fund project (Grant No. L20BTQ003).

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Authors and Affiliations

  1. Library, Shengjing Hospital of China Medical University, No. 36, Sanhao St., Heping Dist., Shenyang, 110004, Liaoning, China

    Yueyang Zhao

  2. Institute of Health Sciences, China Medical University, Shenyang, Liaoning, China

    Lei Cui

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  1. Yueyang Zhao
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  2. Lei Cui
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Correspondence to Yueyang Zhao.

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Zhao, Y., Cui, L. Fusion Matrix–Based Text Similarity Measures for Clustering of Retrieval Results. Scientometrics 128, 1163–1186 (2023). https://doi.org/10.1007/s11192-022-04596-z

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