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Over the past 15 years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semanticdoi:10.1016/j.jbi.2009.02.002 pmid:19232399 pmcid:PMC2750802 fatcat:ehih5ybxqffbbpw4xjkihel3fm