This named entity recognition model detects negation and speculation entities, and negated and speculated concepts:
Neg_cue: negation cue (e.g. no, sin)
Negated: negated entity or event (e.g. sin dolor)
Spec_cue: speculation cue (e.g. posiblemente)
Speculated: speculated entity or event (e.g. posiblemente sobreviva)
The model achieves the following results on the test set (when trained with the training and development set; results are averaged over 5 evaluation rounds):
@article{campillosetal2024,
title = {{Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish}},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},
journal = {BMC Bioinformatics},
year={2024},
publisher={BioMed Central}
}
Intended uses & limitations
Disclosure: This model is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision
This model is intended for a generalist purpose, and may have bias and/or any other undesirable distortions.
Third parties who deploy or provide systems and/or services using any of these models (or using systems based on these models) should note that it is their responsibility to mitigate the risks arising from their use. Third parties, in any event, need to comply with applicable regulations, including regulations concerning the use of artificial intelligence.
The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of these models.
Descargo de responsabilidad: Esta herramienta se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas
La finalidad de este modelo es generalista, y se advierte que puede tener sesgos y/u otro tipo de distorsiones indeseables.
Terceras partes que desplieguen o proporcionen sistemas y/o servicios usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) han tener presente que es su responsabilidad abordar y minimizar los riesgos derivados de su uso. Las terceras partes, en cualquier circunstancia, deben cumplir con la normativa aplicable, incluyendo la normativa que concierne al uso de la inteligencia artificial.
El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos modelos.
Training and evaluation data
The data used for fine-tuning are:
The Negation and Uncertainty in Spanish Corpus (NUBes)
It is a collection of 29 682 sentences (518 068 tokens) from anonymised health records in Spanish, annotated with negation and uncertainty cues and their scopes.
