# Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop

arXiv: https://arxiv.org/abs/2511.06427 · HTML: https://mlsanity.com/paper/2511.06427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://mlsanity.com/paper/2210.01240/) (2022)
- [The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models](https://mlsanity.com/paper/2507.16076/) (2025)
- [MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs](https://mlsanity.com/paper/2509.07622/) (2025)
- [Towards a resource for multilingual lexicons: an MT assisted and human-in-the-loop multilingual parallel corpus with multi-word expression annotation](https://mlsanity.com/paper/2011.03783/) (2020)

## Built on by (most cited first, 3 of 3)

- [HealthcareNLP: where are we and what is next?](https://mlsanity.com/paper/2512.08617/) (2025)
- [DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods](https://mlsanity.com/paper/2605.23052/) (2026)
- [Towards End-to-End Multilingual Metaphor Processing: Integrating Detection, Translation, and Evaluation](https://mlsanity.com/paper/2608.04260/) (2026)

## Abstract (arXiv)

Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients. We extract metaphors used by patients using two data sources: (1) cancer patient storytelling interview data and (2) online forum data, including patients' posts, comments, and questions to professionals. We investigate how current state-of-the-art large language models (LLMs) perform on this task by exploring different prompting strategies such as chain of thought reasoning, few-shot learning, and self-prompting. With a human-in-the-loop setup, we verify the extracted metaphors and compile the outputs into a corpus named HealthQuote.NL. We believe the extracted metaphors can support better patient care, for example shared decision making, improved communication between patients and clinicians, and enhanced patient health literacy. They can also inform the design of personalized care pathways. We share prompts and related resources at https://github.com/4dpicture/HealthQuote.NL