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Semantic relation extraction is one of the frontiers of biomedical naturallanguage processing research. Gold standards are key tools for advancing thisresearch. It is challenging to generate these standards because of the highcost of expert time and the difficulty in establishing agreement betweenannotators. We implemented and evaluated a microtask crowdsourcing approachthat can produce a gold standard for extracting drug-disease relations. Theaggregated crowd judgment agreed with expert annotations from a pre-existingcorpus on 43 of 60 sentences tested. The levels of crowd agreement varied in asimilar manner to the levels of agreement among the original expert annotators.This work rein-forces the power of crowdsourcing in the process of assemblinggold standards for relation extraction. Further, it high-lights the importanceof exposing the levels of agreement between human annotators, expert or crowd,in gold standard corpora as these are reproducible signals indicatingambiguities in the data or in the annotation guidelines.

Exposing ambiguities in a relation-extraction gold standard with crowdsourcing