Crowdsourcing Ground Truth for Medical Relation Extraction

Author:

Dumitrache Anca1,Aroyo Lora2,Welty Chris3

Affiliation:

1. Vrije Universiteit Amsterdam and IBM Center for Advanced Studies Benelux, De Boelelaan, HV Amsterdam, The Netherlands

2. Vrije Universiteit Amsterdam, De Boelelaan, HV Amsterdam, The Netherlands

3. Google Research, New York, NY, USA

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, 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.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Reference31 articles.

1. Lora Aroyo and Chris Welty. 2013. Crowd truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard. Web Science 2013. ACM. Lora Aroyo and Chris Welty. 2013. Crowd truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard. Web Science 2013. ACM.

2. The Three Sides of CrowdTruth

3. What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation

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