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Medical term normalization consists in mapping a piece of text to a largenumber of output classes. Given the small size of the annotated datasets andthe extremely long tail distribution of the concepts, it is of utmostimportance to develop models that are capable to generalize to scarce or unseenconcepts. An important attribute of most target ontologies is theirhierarchical structure. In this paper we introduce a simple and effectivelearning strategy that leverages such information to enhance thegeneralizability of both discriminative and generative models. The evaluationshows that the proposed strategy produces state-of-the-art performance on seenconcepts and consistent improvements on unseen ones, allowing also forefficient zero-shot knowledge transfer across text typologies and datasets.

Generalizing over Long Tail Concepts for Medical Term Normalization