# Dynamic Model for Query-Document Expansion towards Improving Retrieval Relevance

arXiv: https://arxiv.org/abs/2103.10474 · HTML: https://mlsanity.com/paper/2103.10474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis](https://mlsanity.com/paper/2504.05324/) (2025)

## Abstract (arXiv)

Getting relevant information from search engines has been the heart of research works in information retrieval. Query expansion is a retrieval technique that has been studied and proved to yield positive results in relevance. Users are required to express their queries as a shortlist of words, sentences, or questions. With this short format, a huge amount of information is lost in the process of translating the information need from the actual query size since the user cannot convey all his thoughts in a few words. This mostly leads to poor query representation which contributes to undesired retrieval effectiveness. This loss of information has made the study of query expansion technique a strong area of study. This research work focuses on two methods of retrieval for both tweet-length queries and sentence-length queries. Two algorithms have been proposed and the implementation is expected to produce a better relevance retrieval model than most state-the-art relevance models.