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PMID: 19232399 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Review

Empirical distributional semantics: methods and biomedical applications.

Journal of biomedical informatics ·Vol. 42 ·No. 2 ·2009-04-00 ·Pages 390-405

Cohen T, Widdows D

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.

MeSH Terms
Abstracting and Indexing Computational Biology/methods Information Storage and Retrieval/methods MEDLINE Models, Statistical Natural Language Processing Neural Networks, Computer Reproducibility of Results Semantics Software Vocabulary, Controlled
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Cohen Trevor
Center 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
Widdows Dominic
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Article Info
Journal
Journal of biomedical informatics
Abbr.
J Biomed Inform
ISSN
1532-0480
DOI
10.1016/j.jbi.2009.02.002
Email
trevor.cohen@asu.edu
Published
2009-04-00
Epub
2009-00-14
Pages
390-405
Language
English
Region
United States
NLM ID
100970413
PMCID
PMC2750802
Subset
IM
Grants
NLM NIH HHS · R01 LM007894 · United States
NLM NIH HHS · R01 LM007894-03 · United States
NLM NIH HHS · R01 LM07894 · United States
External Links
PubMed source DOI: 10.1016/j.jbi.2009.02.002 PMC Full Text View journal details
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