mlsanityData to 14 Oct 2026

Low-resource Information Extraction with the European Clinical Case Corpus

arXiv 2503.20568 ↗First posted 26 Mar 2025cs.CLcited by 3

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Abstract (arXiv)

We present E3C-3.0, a multilingual dataset in the medical domain, comprising clinical cases annotated with diseases and test-result relations. The dataset includes both native texts in five languages (English, French, Italian, Spanish and Basque) and texts translated and projected from the English source into five target languages (Greek, Italian, Polish, Slovak, and Slovenian). A semi-automatic approach has been implemented, including automatic annotation projection based on Large Language Models (LLMs) and human revision. We present several experiments showing that current state-of-the-art LLMs can benefit from being fine-tuned on the E3C-3.0 dataset. We also show that transfer learning in different languages is very effective, mitigating the scarcity of data. Finally, we compare performance both on native data and on projected data. We release the data at https://huggingface.co/collections/NLP-FBK/e3c-projected-676a7d6221608d60e4e9fd89 .

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 26 Mar 2025.

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18 of 39 references.

References · 18

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6 more
  1. 2023MEDITRON-70B: Scaling Medical Pretraining for Large Language Modelscited by 274
  2. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  3. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 100
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Citing papers per year: 2025: 1; 2026: 2.

3 papers.