Detecting privacy-sensitive events in medical text

In this paper, we present a novel semi-supervised technique for finding privacy-sensitive events in clinical text. Unlike traditional semi-supervised methods, we do not require large amounts of unannotated data. Instead, our approach relies on information contained in the hierarchical structure of a large medical encyclopedia.

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