Document Detail


Using Amazon's Mechanical Turk for Annotating Medical Named Entities.
MedLine Citation:
PMID:  21785667     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Amazon's Mechanical Turk (AMT) service is becoming increasingly popular in Natural Language Processing (NLP) research. In this poster, we report our findings in using AMT to annotate biomedical text extracted from clinical trial descriptions with three entity types: medical condition, medication, and laboratory test. We also describe our observations on AMT workers' annotations.
Authors:
Meliha Yetisgen-Yildiz; Imre Solti; Fei Xia
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium     Volume:  2010     ISSN:  1942-597X     ISO Abbreviation:  -     Publication Date:  2010  
Date Detail:
Created Date:  2011-7-25     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101209213     Medline TA:  AMIA Annu Symp Proc     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1316     Citation Subset:  -    
Affiliation:
Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA.
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MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
K99 LM010227-01//NLM NIH HHS; R00 LM010227-02//NLM NIH HHS; R00 LM010227-03//NLM NIH HHS; R00 LM010227-04//NLM NIH HHS

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