# Deep Neural Networks for Query Expansion using Word Embeddings

arXiv: https://arxiv.org/abs/1811.03514 · HTML: https://mlsanity.com/paper/1811.03514/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?](https://mlsanity.com/paper/2311.09175/) (2023)
- [Multi-Query Video Retrieval](https://mlsanity.com/paper/2201.03639/) (2022)
- [Crafting the Path: Robust Query Rewriting for Information Retrieval](https://mlsanity.com/paper/2407.12529/) (2024)
- [Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology](https://mlsanity.com/paper/2404.06680/) (2024)
- [Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems](https://mlsanity.com/paper/2202.13869/) (2022)
- [Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking](https://mlsanity.com/paper/2606.15998/) (2026)
- [Policy-Grounded Dynamic Facet Suggestions for Job Search](https://mlsanity.com/paper/2605.16479/) (2026)

## Abstract (arXiv)

Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. Previous works have shown that terms selected for query expansion by traditional methods such as pseudo-relevance feedback are not always helpful to the retrieval process. In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods. We then introduce an artificial neural network classifier to predict the usefulness of query expansion terms. This classifier uses term word embeddings as inputs. We perform experiments on four TREC newswire and web collections show that using terms selected by the classifier for expansion significantly improves retrieval performance when compared to competitive baselines. The results are also shown to be more robust than the baselines.