An Information Extraction Study: Take In Mind the Tokenization!
This addresses the efficiency and performance trade-offs in tokenization for biomedical information extraction, though it is incremental as it builds on existing token-free model research.
The study investigated how tokenization affects information extraction from biomedical texts, finding that subword-based models achieve state-of-the-art performance due to inductive bias, while character-based models show promising results, making token-free models feasible.
Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free models remove the traditional tokenization step; however, their efficiency remains unclear. Moreover, the effect of tokenization is relatively unexplored in sequence tagging tasks. To this end, we investigate the impact of tokenization when extracting information from documents and present a comparative study and analysis of subword-based and character-based models. Specifically, we study Information Extraction (IE) from biomedical texts. The main outcome is twofold: tokenization patterns can introduce inductive bias that results in state-of-the-art performance, and the character-based models produce promising results; thus, transitioning to token-free IE models is feasible.