Tung-Le Pan
2026
Capturing Ancient Chinese Sense Induction with Automatic Pipelines
Guan-Yu Tseng | Chunki Lim | Chih-Han Lin | Tung-Le Pan | Yu-Chieh Wang | Lang-Ching Yeh | Shu-Kai Hsieh
Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
Guan-Yu Tseng | Chunki Lim | Chih-Han Lin | Tung-Le Pan | Yu-Chieh Wang | Lang-Ching Yeh | Shu-Kai Hsieh
Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
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.
2019
A Report on the Third VarDial Evaluation Campaign
Marcos Zampieri | Shervin Malmasi | Yves Scherrer | Tanja Samardžić | Francis Tyers | Miikka Silfverberg | Natalia Klyueva | Tung-Le Pan | Chu-Ren Huang | Radu Tudor Ionescu | Andrei M. Butnaru | Tommi Jauhiainen
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
Marcos Zampieri | Shervin Malmasi | Yves Scherrer | Tanja Samardžić | Francis Tyers | Miikka Silfverberg | Natalia Klyueva | Tung-Le Pan | Chu-Ren Huang | Radu Tudor Ionescu | Andrei M. Butnaru | Tommi Jauhiainen
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run – German Dialect Identification (GDI) – and four new tasks – Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.