Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts
Uyen Phan, Nhung Thi Hong Nguyen · 2022
Data augmentation is important in addressing data sparsity and low resources in NLP.Unlike data augmentation for other tasks such as sentence-level and sentence-pair ones, data augmentation for named entity recognition (NER) requires preserving the semantic of entities.To that end, in this paper we propose a simple semantic-based data augmentation method for biomedical NER.Our method leverages semantic information from pre-trained language models for both entity-level and sentence-level.Experimental results on two datasets: i2b2-2010 (English) and VietBioNER (Vietnamese) showed that the proposed method could improve NER performance.