Data Augmentation for Clinical Multilingual Information Extraction
This paper proposes a computationally efficient, word-embedding-based data augmentation strategy using neighbor word replacement that significantly improves Named Entity Recognition and offers modest gains for Entity Linking across five languages, thereby addressing the challenge of limited annotated clinical text in multilingual health informatics.