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        <title>A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese
%A Ma, Xi
%A Hou, Yang
%A Wang, Xuebin
%A Li, Zhenghua
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Mather, Brodie
%Y Dras, Mark
%S Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ma-etal-2025-probabilistic
%X 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.
%U https://aclanthology.org/2025.coling-demos.9/
%P 83-90

Markdown (Informal)

[A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese](https://aclanthology.org/2025.coling-demos.9/) (Ma et al., COLING 2025)

• A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese (Ma et al., COLING 2025)

ACL

• Xi Ma, Yang Hou, Xuebin Wang, and Zhenghua Li. 2025. A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese. In Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations, pages 83–90, Abu Dhabi, UAE. Association for Computational Linguistics.