Crowdsourcing ground truth for medical relation extraction

Anca Dumitrache, Lora Aroyo, Chris Welty

Research output: Contribution to JournalArticleAcademicpeer-review


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, which 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, which account for ambiguity in both human and machine performance on this task.

Original languageEnglish
Article number11
Pages (from-to)1-20
Number of pages20
JournalACM Transactions on Interactive Intelligent Systems
Issue number2
Early online dateJun 2018
Publication statusPublished - Jul 2018


  • Clinical natural language processing
  • Crowd Truth
  • CrowdTruth
  • Ground truth
  • Inter-annotator disagreement
  • Natural language ambiguity
  • Relation extraction


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