  Semantic relation extraction is one of the frontiers of biomedical natural
language processing research. Gold standards are key tools for advancing this
research. It is challenging to generate these standards because of the high
cost of expert time and the difficulty in establishing agreement between
annotators. We implemented and evaluated a microtask crowdsourcing approach
that can produce a gold standard for extracting drug-disease relations. The
aggregated crowd judgment agreed with expert annotations from a pre-existing
corpus on 43 of 60 sentences tested. The levels of crowd agreement varied in a
similar manner to the levels of agreement among the original expert annotators.
This work rein-forces the power of crowdsourcing in the process of assembling
gold standards for relation extraction. Further, it high-lights the importance
of exposing the levels of agreement between human annotators, expert or crowd,
in gold standard corpora as these are reproducible signals indicating
ambiguities in the data or in the annotation guidelines.
