Abstract
MEDLINE is a representative collection of medical documents supplied with original full-text natural-language abstracts as well as with representative keywords (called MeSH-terms) manually selected by the expert annotators from a pre-defined ontology and structured according to their relation to the document. We show how the structured manually assigned semantic descriptions can be combined with the original full-text abstracts to improve quality of clustering the documents into a small number of clusters. As a baseline, we compare our results with clustering using only abstracts or only MeSH-terms. Our experiments show 36% to 47% higher cluster coherence, as well as more refined keywords for the produced clusters.
Work done under partial support of the ITRI of Chung-Ang University, Korean Government (KIPA Professorship for Visiting Faculty Positions in Korea), and Mexican Government (CONACyT, SNI, IPN). The third author is currently on Sabbatical leave at Chung-Ang University.
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© 2004 Springer-Verlag Berlin Heidelberg
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Shin, K., Han, SY., Gelbukh, A. (2004). Advanced Clustering Technique for Medical Data Using Semantic Information. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_33
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DOI: https://doi.org/10.1007/978-3-540-24694-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
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