  We propose a document retrieval method for question answering that represents
documents and questions as weighted centroids of word embeddings and reranks
the retrieved documents with a relaxation of Word Mover's Distance. Using
biomedical questions and documents from BIOASQ, we show that our method is
competitive with PUBMED. With a top-k approximation, our method is fast, and
easily portable to other domains and languages.
