Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slip Scripts
Abstract: This study presents a multi-modal multi-granularity tokenizer specifically designed for analyzing ancient Chinese scripts, focusing on the Chu bamboo slip (CBS) script used during the Spring and Autumn and Warring States period (771-256 BCE) in Ancient China. Considering the complex hierarchical structure of ancient Chinese scripts, where a single character may be a combination of multiple sub-characters, our tokenizer first adopts character detection to locate character boundaries, and then conducts character recognition at both the character and sub-character levels. Moreover, to support the academic community, we have also assembled the first large-scale dataset of CBSs with over 100K annotated character image scans. On the part-of-speech tagging task built on our dataset, using our tokenizer gives a 5.5\% relative improvement in F1-score compared to mainstream sub-word tokenizers. Our work not only aids in further investigations of the specific script but also has the potential to advance research on other forms of ancient Chinese scripts.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: language resources, datasets for low resource languages
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources
Languages Studied: Chinese
Submission Number: 3933
Loading