Knowledge of the left ventricular ejection fraction is critical for the optimal care of patients with heart failure. When a document contains multiple ejection fraction assessments, accurate classification of their contextual use is necessary to filter out historical findings or recommendations and prioritize the assessments for selection of document level ejection fraction information. We present a natural language processing system that classifies the contextual use of both quantitative and qualitative left ventricular ejection fraction assessments in clinical narrative documents. We created support vector machine classifiers with a variety of features extracted from the target assessment, associated concepts, and document section information. The experimental results showed that our classifiers achieved good performance, reaching 95.6% F1-measure for quantitative assessments and 94.2% F₁-measure for qualitative assessments in a five-fold cross-validation evaluation.