| Using Amazon's Mechanical Turk for Annotating Medical Named Entities. | |
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MedLine Citation:
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PMID: 21785667 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Meliha Yetisgen-Yildiz; Imre Solti; Fei Xia |
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Publication Detail:
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Type: JOURNAL ARTICLE |
Journal Detail:
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Title: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Volume: 2010 ISSN: 1942-597X ISO Abbreviation: - Publication Date: 2010 |
Date Detail:
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Created Date: 2011-7-25 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101209213 Medline TA: AMIA Annu Symp Proc Country: - |
Other Details:
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Languages: ENG Pagination: 1316 Citation Subset: - |
Affiliation:
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Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA. |
Export Citation:
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Descriptor/Qualifier:
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| Grant Support | |
ID/Acronym/Agency:
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K99 LM010227-01//NLM NIH HHS; R00 LM010227-02//NLM NIH HHS; R00 LM010227-03//NLM NIH HHS; R00 LM010227-04//NLM NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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