Few Shot NER on Augmented Unstructured Text from Cardiology Records

The principal challenge encountered in the realm of Named-Entity Recognition lies in the acquisition of high-caliber annotated data. In certain languages and specialized domains, the availability of substantial datasets suitable for training models via traditional machine learning methodologies can prove to be a formidable obstacle [10]. In an effort to address this issue, we have explored a Policy-based Active Learning approach aimed at meticulously selecting the most advantageous instances generated through a Data Augmentation procedure [3, 6]. This endeavor was undertaken within the context of a few-shot scenario in the biomedical field. Our study has revealed the superiority of this strategy in comparison to active learning techniques relying on fixed metrics or random instance selection, guaranteeing the privacy of patients from whose medical records the source data were obtained and used. However, it is imperative to note that this approach entails heightened computational demands and necessitates a longer execution duration [7].