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arXiv · 1606.07608

Using Word Embeddings for Automatic Query Expansion

Dwaipayan Roy·Debjyoti Paul·Mandar Mitra·Utpal Garain
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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.

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Dwaipayan Roy, Debjyoti Paul, Mandar Mitra, Utpal Garain. 2016-06-24. Using Word Embeddings for Automatic Query Expansion. https://arxiv.org/abs/1606.07608

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