@article{Pakhomov2010,
abstract = {Automated approaches to measuring semantic similarity and relatedness can provide necessary semantic context information for information retrieval applications and a number of fundamental natural language processing tasks including word sense disambiguation. Challenges for the development of these approaches include the limited availability of validated reference standards and the need for better understanding of the notions of semantic relatedness and similarity in medical vocabulary. We present results of a study in which eight medical residents were asked to judge 724 pairs of medical terms for semantic similarity and relatedness. The results of the study confirm the existence of a measurable mental representation of semantic relatedness between medical terms that is distinct from similarity and independent of the context in which the terms occur. This study produced a validated publicly available dataset for developing automated approaches to measuring semantic relatedness and similarity.},
annote = {
        From Duplicate 1 ( 
        
        
          Semantic Similarity and Relatedness between Clinical Terms: An Experimental Study.
        
        
         - Pakhomov, Serguei; McInnes, Bridget; Adam, Terrence; Liu, Ying; Pedersen, Ted; Melton, Genevieve B )

        
        

        

        

      },
author = {Pakhomov, Serguei and McInnes, Bridget and Adam, Terrence and Liu, Ying and Pedersen, Ted and Melton, Genevieve B},
issn = {1942-597X},
journal = {AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jan,
pages = {572--576},
pmid = {21347043},
title = {{Semantic Similarity and Relatedness between Clinical Terms: An Experimental Study.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3041430\&tool=pmcentrez\&rendertype=abstract},
volume = {2010},
year = {2010}
}