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We propose a method for assigning upper level Gene Ontology terms (GO categories) to genes using relevant documents. This method represents each gene as a vector using relevant documents to the gene. Then, binary classifiers are made for the GO categories using such supervised learning methods as support vector machines and maximum entropy method. We applied this method for assigning GO categories to yeast genes and achieved an average F-measure of 0.67, which is 0.3 higher than the existingdoi:10.1109/csb.2004.1332476 dblp:conf/csb/IzumitaniTKM04 fatcat:o4zhjcchf5fublzpaglgbl44ui