Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
J Biomed Inform. 2009 Apr;42(2):390-405. doi: 10.1016/j.jbi.2009.02.002. Epub 2009 Feb 14.

Empirical distributional semantics: methods and biomedical applications.

Author information

  • 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.

PMID:
19232399
[PubMed - indexed for MEDLINE]
PMCID:
PMC2750802
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for PubMed Central
    Loading ...
    Write to the Help Desk