Automatic prediction of evidence-based recommendations via sentence-level polarity classification

We propose a supervised classification approach for automatically determining the polarities of medical sentences. Our polarity classification approach is context sensitive, meaning that the same sentence may have differing polarities depending on the context. Using a set of carefully selected features, we achieve 84.7% accuracy, which is significantly better than current state-of-the-art for the polarity classification task. Our analyses and experiments on a specialised corpus indicate that automatic polarity classification of key sentences can be utilised to generate evidence-based recommendations.7 page(s