Abstract

The 2014 BioASQ challenge 2a tasks participants with assigning semantic tags to biomedical journal abstracts. We present a system that uses Latent Semantic Analysis to identify semantically similar documents in MEDLINE to an unlabeled abstract, and then uses a novel ranking scheme to select a list of MeSH headers from candidates drawn from the most similar documents. Our approach achieved good precision, but suffered in terms of recall. We describe several possible strategies to improve our system's performance.

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