Researchers have developed a method for segmenting words in the Tangut language, which is an extinct script that doesn't explicitly mark word boundaries. The team used a combination of expert-annotated segments, traditional lexicons, and unlabeled text to train their model. Their framework achieved high accuracy in identifying word boundaries, with a F1 score of 0.91. This research demonstrates the potential for generalizing beyond limited supervised vocabulary in language processing tasks. --- Why it matters: This work matters because it provides a novel approach to tackling the challenge of word segmentation in languages without explicit word boundary markers. The techniques developed here could be applied to other languages with similar characteristics, opening up new avenues for natural language processing research and applications. Source: https://arxiv.org/abs/2608.18437