# Exploring Query Categorisation for Query Expansion: A Study

arXiv: https://arxiv.org/abs/1509.05567 · HTML: https://mlsanity.com/paper/1509.05567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2015.

## Built on by (most cited first, 2 of 2)

- [A Systematic Review of Automated Query Reformulations in Source Code Search](https://mlsanity.com/paper/2108.09646/) (2021)
- [Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce](https://mlsanity.com/paper/2301.00036/) (2022)

## Abstract (arXiv)

The vocabulary mismatch problem is one of the important challenges facing traditional keyword-based Information Retrieval Systems. The aim of query expansion (QE) is to reduce this query-document mismatch by adding related or synonymous words or phrases to the query. Several existing query expansion algorithms have proved their merit, but they are not uniformly beneficial for all kinds of queries. Our long-term goal is to formulate methods for applying QE techniques tailored to individual queries, rather than applying the same general QE method to all queries. As an initial step, we have proposed a taxonomy of query classes (from a QE perspective) in this report. We have discussed the properties of each query class with examples. We have also discussed some QE strategies that might be effective for each query category. In future work, we intend to test the proposed techniques using standard datasets, and to explore automatic query categorisation methods.