@inproceedings{ma-etal-2025-probabilistic,
    title = "A Probabilistic Toolkit for Multi-grained Word Segmentation in {C}hinese",
    author = "Ma, Xi  and
      Hou, Yang  and
      Wang, Xuebin  and
      Li, Zhenghua",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Mather, Brodie  and
      Dras, Mark",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-demos.9/",
    pages = "83--90",
    abstract = "It is practically useful to provide consistent and reliable word segmentation results from different criteria at the same time, which is formulated as the multi-grained word segmentation (MWS) task. This paper describes a probabilistic toolkit for MWS in Chinese. We propose a new MWS approach based on the standard MTL framework. We adopt semi-Markov CRF for single-grained word segmentation (SWS), which can produce marginal probabilities of words during inference. For sentences that contain conflicts among SWS results, we employ the CKY decoding algorithm to resolve conflicts.Our resulting MWS tree can provide the criteria information of words, along with the probabilities. Moreover, we follow the works in SWS, and propose a simple strategy to exploit naturally annotated data for MWS, leading to substantial improvement of MWS performance in the cross-domain scenario."
}