Transformer-Based Models for Text Representation and Processing

Transformer-based models have revolutionized natural language processing (NLP) in recent years, particularly in the biomedical domain. In this chapter, we explore transformer-based models for text representation in biomedical NLP, with a specific focus on BERT and BioBERT. We begin by discussing the unique challenges of working with biomedical text, highlighting the need for more powerful models. We then delve into the architecture of BERT and its pre-training techniques, including masked LM (MLM) and next sentence prediction (NSP). We also explore BioBERT, a pre-trained model specifically designed for biomedical text and its pre-training and fine-tuning processes. Throughout the chapter, we demonstrate the advantages of transformer-based models over traditional approaches in biomedical NLP and discuss the research challenges that still need to be addressed. This chapter provides a comprehensive guide to transformer-based models and their applications in biomedical text representation, making it an invaluable resource for researchers and practitioners in this field.