ゲノム情報科学研究教育機構  アブストラクト
Date February 2, 2009
Speaker Dr. Shanfeng Zhu, Associate Professor, Fudan University, China
Title Enhancing MEDLINE Document Clustering by Incorporating MeSH Semantic Similarity
Abstract 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) terms is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be clustered. However, current approaches of using MeSH terms 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. Our new strategy includes three key points. First, we develop a sound method for measuring the semantic similarity between two documents over the MeSH ontology. Second, we combine both the semantic and the content similarities to generate the integrated similarity matrix between documents. Third, we apply a spectral approach to clustering documents over the integrated similarity matrix. 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 which 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.
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