Improving Pseudo-Relevance Feedback With Neural Network-Based Word Representations

In information retrieval, query expansion methods, such as pseudo-relevance feedback, are designed to enrich users' queries with relevant terms for comprehensively interpreting the desired information. One of the key issues for query expansion is how to obtain high-quality expansion terms to capture the information needs. Recent advances in neural network language models have demonstrated that these models can learn powerful distributed word representations, which have been successfully applied to solve various natural language processing tasks. In this paper, we propose a novel query expansion framework based on neural network-based word representations. Our framework first selects abundant candidate expansion terms using a modified term-dependency method and then generates term features for candidate terms based on word representations to encode relationships between given queries and corresponding candidate terms. Furthermore, we adopt learning-to-rank methods to train term-ranking models with the generated features for term refinement. We conduct extensive experiments to examine the performance of the learned term-ranking models and compare the effectiveness of the representation-based and context-based features for selecting relevant expansion terms. Experimental results using four TREC collections show that neural network-based word representations are effective in query expansion and can significantly improve retrieval performance.