Using Word Embeddings for Automatic Query Expansion
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.
Cite
@article{arxiv.1606.07608,
title = {Using Word Embeddings for Automatic Query Expansion},
author = {Dwaipayan Roy and Debjyoti Paul and Mandar Mitra and Utpal Garain},
journal= {arXiv preprint arXiv:1606.07608},
year = {2016}
}
Comments
5 pages, 3 tables, 1 figure. Neu-IR '16 SIGIR Workshop on Neural Information Retrieval July 21, 2016, Pisa, Italy