  The main approach of traditional information retrieval (IR) is to examine how
many 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 few
words from a query are found. The semantic analysis methods such as LSA (latent
semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to
address the issue, but their performance is not superior compared to common IR
approaches. Here we present a query-document similarity measure motivated by
the Word Mover's Distance. Unlike other similarity measures, the proposed
method relies on neural word embeddings to compute the distance between words.
This process helps identify related words when no direct matches are found
between a query and a document. Our method is efficient and straightforward to
implement. The experimental results on TREC Genomics data show that our
approach outperforms the BM25 ranking function by an average of 12% in mean
average precision. Furthermore, for a real-world dataset collected from the
PubMed search logs, we combine the semantic measure with BM25 using a learning
to rank method, which leads to improved ranking scores by up to 25%. This
experiment demonstrates that the proposed approach and BM25 nicely complement
each other and together produce superior performance.
