Unsupervised Tokenization

Unsupervised tokenization aims to automatically divide text into meaningful units (tokens) without relying on pre-existing dictionaries or labeled data, a crucial step in natural language processing for low-resource languages. Recent research focuses on optimizing unsupervised methods by exploring novel metrics beyond traditional statistical measures, such as those based on transition probabilities or information-theoretic concepts, to improve tokenization accuracy across diverse languages. These advancements offer the potential for more robust and efficient language processing, particularly beneficial for languages lacking extensive annotated resources, and contribute to a deeper understanding of how languages evolve and structure information.