# Query Expansion with Locally-Trained Word Embeddings

arXiv: https://arxiv.org/abs/1605.07891 · HTML: https://mlsanity.com/paper/1605.07891/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 17 of 17)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List](https://mlsanity.com/paper/2104.07186/) (2021)
- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training](https://mlsanity.com/paper/2306.07193/) (2023)
- [A Query-Driven Topic Model](https://mlsanity.com/paper/2106.07346/) (2021)
- ["A Passage to India": Pre-trained Word Embeddings for Indian Languages](https://mlsanity.com/paper/2112.13800/) (2021)
- [Event-Centric Query Expansion in Web Search](https://mlsanity.com/paper/2305.19019/) (2023)
- [LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback](https://mlsanity.com/paper/2204.11545/) (2022)
- [Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce](https://mlsanity.com/paper/2301.00036/) (2022)
- [Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking](https://mlsanity.com/paper/2606.15998/) (2026)
- [Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviews](https://mlsanity.com/paper/2112.14318/) (2021)
- [A bilingual approach to specialised adjectives through word embeddings in the karstology domain](https://mlsanity.com/paper/2203.16885/) (2022)
- [A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis](https://mlsanity.com/paper/2205.01600/) (2022)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)
- [LGDE: Local Graph-based Dictionary Expansion](https://mlsanity.com/paper/2405.07764/) (2024)
- [GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval](https://mlsanity.com/paper/2506.04762/) (2025)
- [Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective](https://mlsanity.com/paper/2608.08445/) (2026)

## Abstract (arXiv)

Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships. We study the use of term relatedness in the context of query expansion for ad hoc information retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when trained globally, underperform corpus and query specific embeddings for retrieval tasks. These results suggest that other tasks benefiting from global embeddings may also benefit from local embeddings.