Multi-Tiered Cantonese Word Segmentation

This data is the subset of the Hong Kong Cantonese Corpus (HKCanCor) that has been re-segmented by the multi-tiered word segmentation scheme described in the following paper:

• 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, Italy. ELRA and ICCL.

@inproceedings{lam-etal-2024-multi-tiered,
    title = "Multi-Tiered {C}antonese Word Segmentation",
    author = "Lam, Charles  and
      Lau, Chaak-ming  and
      Lee, Jackson L.",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italy",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1047",
    pages = "11993--12002",
    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.",
}

This data is released under the CC BY 4.0 license, the same license associated with the source HKCanCor data.