@article{
 title = {Preliminary Experience with Amazon ’ s Mechanical Turk for Annotating Medical Named Entities},
 type = {article},
 year = {2010},
 pages = {180-183},
 id = {543ee9b7-10a6-37d0-bb04-5b4352643667},
 created = {2012-01-21T12:35:31.000Z},
 file_attached = {true},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {corpus annotation},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yetisgen-yildiz2010},
 private_publication = {false},
 abstract = {Amazon’s Mechanical Turk (MTurk) service is becoming increasingly popular in Natural Language Processing (NLP) research. In this paper, we report our findings in using MTurk to annotate medical text extracted from clini- cal trial descriptions with three entity types: medical condition, medication, and laboratory test. We compared MTurk annotations with a gold standard manually created by a domain expert. Based on the good performance re- sults, we conclude that MTurk is a very prom- ising tool for annotating large-scale corpora for biomedical NLP tasks.},
 bibtype = {article},
 author = {Yetisgen-yildiz, Meliha and Solti, Imre and Xia, Fei and Halgrim, Scott Russell},
 journal = {Computational Linguistics},
 number = {June}
}

{"_id":"2vKZK3vW3w6jFYx9y","bibbaseid":"yetisgenyildiz-solti-xia-halgrim-preliminaryexperiencewithamazonsmechanicalturkforannotatingmedicalnamedentities-2010","authorIDs":[],"author_short":["Yetisgen-yildiz, M.","Solti, I.","Xia, F.","Halgrim, S., R."],"bibdata":{"title":"Preliminary Experience with Amazon ’ s Mechanical Turk for Annotating Medical Named Entities","type":"article","year":"2010","pages":"180-183","id":"543ee9b7-10a6-37d0-bb04-5b4352643667","created":"2012-01-21T12:35:31.000Z","file_attached":"true","profile_id":"5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6","group_id":"066b42c8-f712-3fc3-abb2-225c158d2704","last_modified":"2017-03-14T14:36:19.698Z","tags":"corpus annotation","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Yetisgen-yildiz2010","private_publication":false,"abstract":"Amazon’s Mechanical Turk (MTurk) service is becoming increasingly popular in Natural Language Processing (NLP) research. In this paper, we report our findings in using MTurk to annotate medical text extracted from clini- cal trial descriptions with three entity types: medical condition, medication, and laboratory test. We compared MTurk annotations with a gold standard manually created by a domain expert. Based on the good performance re- sults, we conclude that MTurk is a very prom- ising tool for annotating large-scale corpora for biomedical NLP tasks.","bibtype":"article","author":"Yetisgen-yildiz, Meliha and Solti, Imre and Xia, Fei and Halgrim, Scott Russell","journal":"Computational Linguistics","number":"June","bibtex":"@article{\n title = {Preliminary Experience with Amazon ’ s Mechanical Turk for Annotating Medical Named Entities},\n type = {article},\n year = {2010},\n pages = {180-183},\n id = {543ee9b7-10a6-37d0-bb04-5b4352643667},\n created = {2012-01-21T12:35:31.000Z},\n file_attached = {true},\n profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},\n group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},\n last_modified = {2017-03-14T14:36:19.698Z},\n tags = {corpus annotation},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Yetisgen-yildiz2010},\n private_publication = {false},\n abstract = {Amazon’s Mechanical Turk (MTurk) service is becoming increasingly popular in Natural Language Processing (NLP) research. In this paper, we report our findings in using MTurk to annotate medical text extracted from clini- cal trial descriptions with three entity types: medical condition, medication, and laboratory test. We compared MTurk annotations with a gold standard manually created by a domain expert. Based on the good performance re- sults, we conclude that MTurk is a very prom- ising tool for annotating large-scale corpora for biomedical NLP tasks.},\n bibtype = {article},\n author = {Yetisgen-yildiz, Meliha and Solti, Imre and Xia, Fei and Halgrim, Scott Russell},\n journal = {Computational Linguistics},\n number = {June}\n}","author_short":["Yetisgen-yildiz, M.","Solti, I.","Xia, F.","Halgrim, S., R."],"urls":{"Paper":"https://bibbase.org/service/mendeley/bfdabac2-d7f2-3c5b-aa7a-06431c0ae35e/file/1750b08d-d4e5-1bf9-0f55-146866166f8d/2010-Preliminary_Experience_with_Amazon__s_Mechanical_Turk_for_Annotating_Medical_Named_Entities.pdf.pdf"},"bibbaseid":"yetisgenyildiz-solti-xia-halgrim-preliminaryexperiencewithamazonsmechanicalturkforannotatingmedicalnamedentities-2010","role":"author","downloads":0,"html":""},"bibtype":"article","creationDate":"2020-02-06T23:48:11.918Z","downloads":0,"keywords":[],"search_terms":["preliminary","experience","amazon","mechanical","turk","annotating","medical","named","entities","yetisgen-yildiz","solti","xia","halgrim"],"title":"Preliminary Experience with Amazon ’ s Mechanical Turk for Annotating Medical Named Entities","year":2010}