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Capturing Ancient Chinese Sense Induction with Automatic Pipelines

While the study of diachronic semantic change has advanced alongside recent computational developments, structured lexical resources that reflect semantic evolution remain scarce for many languages, including Ancient Chinese. By systematizing the diachronic transformations within the Chinese Text Project (ctext, a large corpus of Ancient Chinese), we aim to bridge the gap between traditional philological inquiry and contemporary computational linguistics. This study proposes a pipeline that extracts contextualized embeddings from GujiBERT-fan, a language model pre-trained on pre-modern Chinese, and applies dynamic hierarchical clustering to identify distinct senses across historical periods. The pipeline operates at two levels: a global clustering that aggregates data across all periods to capture the full semantic space, and local clustering within each dynasty to reveal period-specific usage patterns. We test the pipeline with a pilot study on the character 手 (shǒu, "hand") across eight dynastic periods, covering over 185,000 occurrences. The results show that the pipeline can capture the diachronic shift from concrete to abstract senses, demonstrating its potential as a scalable method for mapping semantic evolution in historical languages.