Data Augmentation: Enhancing Named Entity Recognition Performance on Swedish Medical Texts

Abstract

Named Entity Recognition (NER) refers to the task of locating relevant information within text sequences. Within the medical domain, it can benefit applications such as de-identifying patient records or extracting valuable data for other downstream tasks. However, achieving a highly reliable system can be challenging, particularly for low-resource languages such as Swedish, where the amount of accessible text data is relatively small compared to larger languages. To tackle this challenge, data augmentation has emerged as a promising solution, where new data samples can artificially be generated. This study explores various BERT models and data augmentation techniques to identify the best-performing method for performing NER on Swedish patient records from Karolinska University Hospital, namely the Stockholm EPR PHI Pseudo Corpus. Our findings reveal that the BERT model, SweDeClin-BERT, was the highest-performing method, yielding the highest F1 score. Additionally, we demonstrate that data augmentation can further enhance performance, especially in the context of smaller datasets. By deploying data augmentation to a portion of 50% of the training data, we demonstrate comparable results to using 100% of the original training data without any augmentation.