# Using Word Embeddings for Automatic Query Expansion

arXiv: https://arxiv.org/abs/1606.07608 · HTML: https://mlsanity.com/paper/1606.07608/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 14 of 14)

- [Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?](https://mlsanity.com/paper/2311.09175/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Crafting the Path: Robust Query Rewriting for Information Retrieval](https://mlsanity.com/paper/2407.12529/) (2024)
- [A New Query Expansion Approach via Agent-Mediated Dialogic Inquiry](https://mlsanity.com/paper/2502.08557/) (2025)
- [Hybrid Retrieval-Augmented Generation for Robust Multilingual Document Question Answering](https://mlsanity.com/paper/2512.12694/) (2025)
- [Finetuning Transformer Models to Build ASAG System](https://mlsanity.com/paper/2109.12300/) (2021)
- [GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval](https://mlsanity.com/paper/2409.10909/) (2024)
- [Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking](https://mlsanity.com/paper/2606.15998/) (2026)
- [ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation](https://mlsanity.com/paper/2109.00729/) (2021)
- [LGDE: Local Graph-based Dictionary Expansion](https://mlsanity.com/paper/2405.07764/) (2024)
- [Passage Query Methods for Retrieval and Reranking in Conversational Agents](https://mlsanity.com/paper/2503.00238/) (2025)
- [GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval](https://mlsanity.com/paper/2506.04762/) (2025)
- [Policy-Grounded Dynamic Facet Suggestions for Job Search](https://mlsanity.com/paper/2605.16479/) (2026)
- [Towards Explaining Query Expansion Performance in Information Retrieval](https://mlsanity.com/paper/2610.09724/) (2026)

## Abstract (arXiv)

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.