Abstract
Word segmentation for Chinese text data is essential for compiling corpora and any other tasks where the notion of “word” is assumed, since Chinese orthography does not have conventional word boundaries as languages such as English do. A perennial issue, however, is that there is no consensus about the definition of “word” in Chinese, which makes word segmentation challenging. Recent work in Chinese word segmentation has begun to embrace the idea of multiple word segmentation possibilities. In a similar spirit, this paper focuses on Cantonese, another major Chinese variety. We propose a linguistically motivated, multi-tiered word segmentation system for Cantonese, and release a Cantonese corpus of 150,000 characters word-segmented by this proposal. Our work will be of interest to researchers whose work involves Cantonese corpus data.- Anthology ID:
- 2024.lrec-main.1047
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 11993–12002
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1047
- DOI:
- Cite (ACL):
- Charles Lam, Chaak-ming Lau, and Jackson L. Lee. 2024. Multi-Tiered Cantonese Word Segmentation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11993–12002, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Multi-Tiered Cantonese Word Segmentation (Lam et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1047.pdf