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 calculate the distance between words. Our method is efficient and straightforward to implement. The experimental results on TREC and PubMed show that our approach provides significantly better performance than BM25. We also discuss the pros and cons of our approach and show that there is a synergy effect when the word embedding measure is combined with the BM25 function.

提出了一种基于Word Mover's Distance计算单词间距离的查询-文档相似度测量方法，利用神经词嵌入在检索无直接匹配的情况下发现相关词，并将其与BM25相结合，结果在TREC Genomics数据上平均精度平均提高12％，在来自PubMed搜索日志的真实数据集上，结合学习排序的方法，正确率提高了25％，表明该方法与BM25的结合可以产生更出色的性能。

弥合差距：有效映射PubMed查询与文档的语义相似度度量