Enhancing Location Entity Recognition in Spanish Medical Texts by Leveraging Domain Language Models and Data Augmentation
This work focuses on the automatic recognition of location entities in Spanish clinical reports, using the MEDDOPLACE challenge (IberLEF 2023) as the experimental framework. We evaluated both general-domain pre-trained models and biomedical-specific models. Furthermore, we explored data augmentation...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad Nacional de Educación a Distancia |
| Repositorio: | e-spacio (DSpace). Repositorio Institucional de la UNED |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:e-spacio(ds_::edf98fdb39b7c5070ec9e80525d9ba1f |
| Acceso en línea: | https://hdl.handle.net/20.500.14468/32213 |
| Access Level: | acceso abierto |
| Palabra clave: | 1203.18 Sistemas de información, diseño y componentes location entity recognition data augmentation medical domain domain-specific language models reconocimiento de entidades de lugar aumento de datos dominio médico modelos de lenguaje de dominio espec´ıfico |
| Sumario: | This work focuses on the automatic recognition of location entities in Spanish clinical reports, using the MEDDOPLACE challenge (IberLEF 2023) as the experimental framework. We evaluated both general-domain pre-trained models and biomedical-specific models. Furthermore, we explored data augmentation techniques via back-translation and LLM-based paraphrase generation. Our results outperform previous state-of-the-art approaches, demonstrating the effectiveness of combining these data augmentation strategies with pre-trained clinical domain models. |
|---|