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The amount of publicly available biomedical literature has been growingrapidly in recent years, yet question answering systems still struggle toexploit the full potential of this source of data. In a preliminary processingstep, many question answering systems rely on retrieval models for identifyingrelevant documents and passages. This paper proposes a weighted cosine distanceretrieval scheme based on neural network word embeddings. Our experiments arebased on publicly available data and tasks from the BioASQ biomedical questionanswering challenge and demonstrate significant performance gains over a widerange of state-of-the-art models.

Biomedical Question Answering via Weighted Neural Network Passage Retrieval