  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 methods such as pseudo-relevance
feedback 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 to
predict the usefulness of query expansion terms. This classifier uses term word
embeddings as inputs. We perform experiments on four TREC newswire and web
collections show that using terms selected by the classifier for expansion
significantly improves retrieval performance when compared to competitive
baselines. The results are also shown to be more robust than the baselines.
