Using Amazon’s Mechanical Turk for Annotating Medical Named Entities
Meliha Yetisgen-Yildiz, I. Solti, Fei Xia
2010AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
tlooto Summary
Findings in using AMT to annotate biomedical text extracted from clinical trial descriptions with three entity types: medical condition, medication, and laboratory test are reported.
Abstract
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.
Citation format
YETISGEN-YILDIZ, Meliha; SOLTI, I.; XIA, Fei. Using amazon’s mechanical turk for annotating medical named entities. AMIA... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2010, 2010: 1316–1316.