Using Word Embeddings for Automatic Query Expansion

TL;DR

This paper introduces a neural embedding-based framework for automatic query expansion using word2vec, demonstrating improved retrieval performance over standard methods but less effective than statistical feedback techniques.

Contribution

Proposes a novel unsupervised query expansion method using word2vec embeddings and K-nearest neighbors, with comprehensive experimental evaluation.

Findings

Significant improvement over standard term-overlapping retrieval methods

Performs similarly with and without feedback information

Less effective than statistical co-occurrence based feedback methods like RM3

Abstract

In this paper a framework for Automatic Query Expansion (AQE) is proposed using distributed neural language model word2vec. Using semantic and contextual relation in a distributed and unsupervised framework, word2vec learns a low dimensional embedding for each vocabulary entry. Using such a framework, we devise a query expansion technique, where related terms to a query are obtained by K-nearest neighbor approach. We explore the performance of the AQE methods, with and without feedback query expansion, and a variant of simple K-nearest neighbor in the proposed framework. Experiments on standard TREC ad-hoc data (Disk 4, 5 with query sets 301-450, 601-700) and web data (WT10G data with query set 451-550) shows significant improvement over standard term-overlapping based retrieval methods. However the proposed method fails to achieve comparable performance with statistical co-occurrence based feedback method such as RM3. We have also found that the word2vec based query expansion methods perform similarly with and without any feedback information.