We investigate automatic classification of speculative language ('hedging'), in biomedical text using weakly supervised machine learning. Our contributions include a precise description of the task with annotation guidelines, analysis and discussion, a probabilistic weakly supervised learning model, and experimental evaluation of the methods presented. We show that hedge classification is feasible using weakly supervised ML, and point toward avenues for future research.
Medlock and Briscoe REF extend this work and discuss the specificities of hedge classification as a weakly supervised machine learning task and present a probabilistic learning model.
Weakly Supervised Learning for Hedge Classification in Scientific Literature
45th Annual Meeting of the Association of Computational Linguistics
Computer Science