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.
A series of information retrieval experiments was carried out with a computer installed in a medical practice setting for relatively inexperienced physician end-users. Using a commercial MEDLINE product based on the vector space model, these physicians searched just as effectively as more experienced searchers using Boolean searching. The results of this experiment were subsequently used to create a new large medical test collection, which was used in experiments with the SMART retrieval system to obtain baseline performance data as well as compare SMART with the other searchers.