Title: Crowdsourcing Ground Truth for Medical Relation Extraction,
Abstract: Cognitive computing systems require human labeled data for evaluation, and
often for training. The standard practice used in gathering this data minimizes
disagreement between annotators, and we have found this results in data that
fails to account for the ambiguity inherent in language. We have proposed the
CrowdTruth method for collecting ground truth through crowdsourcing, that
reconsiders the role of people in machine learning based on the observation
that disagreement between annotators provides a useful signal for phenomena
such as ambiguity in the text. We report on using this method to build an
annotated data set for medical relation extraction for the $cause$ and $treat$
relations, and how this data performed in a supervised training experiment. We
demonstrate that by modeling ambiguity, labeled data gathered from crowd
workers can (1) reach the level of quality of domain experts for this task
while reducing the cost, and (2) provide better training data at scale than
distant supervision. We further propose and validate new weighted measures for
precision, recall, and F-measure, that account for ambiguity in both human and
machine performance on this task.