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The main approach of traditional information retrieval (IR) is to examine howmany words from a query appear in a document. A drawback of this approach,however, is that it may fail to detect relevant documents where no or only fewwords from a query are found. The semantic analysis methods such as LSA (latentsemantic analysis) and LDA (latent Dirichlet allocation) have been proposed toaddress the issue, but their performance is not superior compared to common IRapproaches. Here we present a query-document similarity measure motivated bythe Word Mover's Distance. Unlike other similarity measures, the proposedmethod relies on neural word embeddings to compute the distance between words.This process helps identify related words when no direct matches are foundbetween a query and a document. Our method is efficient and straightforward toimplement. The experimental results on TREC Genomics data show that ourapproach outperforms the BM25 ranking function by an average of 12% in meanaverage precision. Furthermore, for a real-world dataset collected from thePubMed search logs, we combine the semantic measure with BM25 using a learningto rank method, which leads to improved ranking scores by up to 25%. Thisexperiment demonstrates that the proposed approach and BM25 nicely complementeach other and together produce superior performance.

Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents