Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity
Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity
复制标题
通过合并 MeSH 语义相似性增强 MEDLINE 文档聚类
DOI：
10.1093/bioinformatics/btp338
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发表时间：
2009-08-01
期刊：
影响因子：
5.8
通讯作者：
Mamitsuka, Hiroshi
中科院分区：
文献类型：
--
作者：
Zhu, Shanfeng;Zeng, Jia;Mamitsuka, Hiroshi
Motivation: Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the inner-product of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be clustered. However, current approaches of using MeSH thesaurus have two serious limitations: first, important semantic information may be lost when generating MeSH concept vectors, and second, the content information of the original text has been discarded.Methods: Our new strategy includes three key points. First, we develop a sound method for measuring the semantic similarity between two documents over the MeSH thesaurus. Second, we combine both the semantic and content similarities to generate the integrated similarity matrix between documents. Third, we apply a spectral approach to clustering documents over the integrated similarity matrix.Results: Using various 100 datasets of MEDLINE records, we conduct extensive experiments with changing alternative measures and parameters. Experimental results show that integrating the semantic and content similarities outperforms the case of using only one of the two similarities, being statistically significant. We further find the best parameter setting that is consistent over all experimental conditions conducted. We finally show a typical example of resultant clusters, confirming the effectiveness of our strategy in improving MEDLINE document clustering.
