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Updated: Jun 25, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Empirical distributional semantics: methods and biomedical applications
Trevor Cohen1, Dominic Widdows
1Center for Decision Making and Cognition, Department of Biomedical Informatics, School of Computing and Informatics, Arizona State University, 425 N, 5th Street, Phoenix, AZ 85004-2157, USA. trevor.cohen@asu.edu
Abstract:
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 semantic relatedness from free text, as well as their evaluation in a variety of biomedical and other applications. Recent methodological developments, and their applicability to several existing applications are also discussed.
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