  The amount of publicly available biomedical literature has been growing
rapidly in recent years, yet question answering systems still struggle to
exploit the full potential of this source of data. In a preliminary processing
step, many question answering systems rely on retrieval models for identifying
relevant documents and passages. This paper proposes a weighted cosine distance
retrieval scheme based on neural network word embeddings. Our experiments are
based on publicly available data and tasks from the BioASQ biomedical question
answering challenge and demonstrate significant performance gains over a wide
range of state-of-the-art models.
