Optimizing Biomedical Text Processing: A Comparative Analysis of Tokenization Methods and Context-Aware Representation Learning.

Natural Language Processing (NLP) workflows in biomedical domains face unique challenges due to specialized terminologies and the need for high precision in downstream applications. This study presents a systematic framework for preprocessing and analyzing biomedical texts, with a focus on evaluating tokenization strategies and their impact on representation learning. We have proposed a dual-phase approach: first, benchmarking various tokenizers across efficiency and domain-specific accuracy metrics; second, integrating context-aware embedding techniques to enhance semantic capture. Our experiments reveal that SciSpacy outperforms conventional tokenizers in biomedical term recognition despite computational trade-offs, while custom-trained BPE models achieve a 22