Automatic identification of potentially contradictory claims to support systematic reviews

Medical literature suffers from the existence of contradictory studies that make incompatible claims about the same research question. This research introduces an automatic system that detects contradiction between research claims using their assertion value with respect to a question. The system uses a machine learning algorithm (SVM) to construct a classifier that uses multiple linguistic features to recognise a claim´s assertion value. The classifier is developed using a dataset consisting of 258 claims distributed in 24 groups, where each group answers a single research question. The classifier achieved ROC Score of 89% and precision/recall of 87.3%, compared against a baseline of 68%. The system enables researchers carrying out systematic reviews to visually identify potentially contradictory research claims.