Deep neural networks for query expansion using word embeddings

Abstract

Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. Previous works have shown that terms selected for query expansion by traditional pseudo-relevance feedback methods such as mixture model are not always helpful to the retrieval process. In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods. We then introduce an artificial neural network classifier, which uses term word embeddings as input, to predict the usefulness of query expansion terms. Experiments on four TREC newswire and web collections show that using terms selected by the classifier for expansion significantly improves retrieval performance compared to competitive baselines. The results are also shown to be more robust than the baselines.