Document Detail

Comprehending technical texts: predicting and defining unfamiliar terms.
MedLine Citation:
PMID:  17238339     Owner:  NLM     Status:  MEDLINE    
We investigate how to improve access to medical literature for health consumers. Our focus is on medical terminology. We present a method to predict automatically in a given text which medical terms are unlikely to be understood by a lay reader. Our method, which is linguistically motivated and fully unsupervised, relies on how common a specific term is in texts that we already know are familiar to a lay reader. Once a term is identified as unfamiliar, an appropriate definition is mined from the Web to be provided to the reader. Our experiments show that the prediction and the addition of definitions significantly improve lay readers' comprehension of sentences containing technical medical terms.
Noemie Elhadad
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium     Volume:  -     ISSN:  1942-597X     ISO Abbreviation:  -     Publication Date:  2006  
Date Detail:
Created Date:  2007-01-22     Completed Date:  2007-09-28     Revised Date:  2009-11-18    
Medline Journal Info:
Nlm Unique ID:  101209213     Medline TA:  AMIA Annu Symp Proc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  239-43     Citation Subset:  IM    
Department of Computer Science, City College of New York, New York, NY, USA.
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MeSH Terms
Educational Status
Terminology as Topic*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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