# Interactive query expansion for professional search applications

arXiv: https://arxiv.org/abs/2106.13528 · HTML: https://mlsanity.com/paper/2106.13528/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks](https://mlsanity.com/paper/2407.13511/) (2024)
- [Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance of Current GPT Models in Biomedical Tasks](https://mlsanity.com/paper/2306.16108/) (2023)
- [Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025](https://mlsanity.com/paper/2508.05366/) (2025)
- [A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts](https://mlsanity.com/paper/2204.02712/) (2022)
- [Keyword Embeddings for Query Suggestion](https://mlsanity.com/paper/2301.08006/) (2023)

## Abstract (arXiv)

Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expert knowledge to formulate accurate search strategies. Interactive features such as query expansion can play a key role in supporting these tasks. However, generating query suggestions within a professional search context requires that consideration be given to the specialist, structured nature of the search strategies they employ. In this paper, we investigate a variety of query expansion methods applied to a collection of Boolean search strategies used in a variety of real-world professional search tasks. The results demonstrate the utility of context-free distributional language models and the value of using linguistic cues such as ngram order to optimise the balance between precision and recall.