@inproceedings{huang-etal-2020-context,
    title = "Context-Aware Word Segmentation for {C}hinese Real-World Discourse",
    author = "Huang, Kaiyu  and
      Liu, Junpeng  and
      Cao, Jingxiang  and
      Huang, Degen",
    editor = "Liu, Qun  and
      Xiong, Deyi  and
      Ge, Shili  and
      Zhang, Xiaojun",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.iwdp-1.5/",
    doi = "10.18653/v1/2020.iwdp-1.5",
    pages = "22--28",
    abstract = "Previous neural approaches achieve significant progress for Chinese word segmentation (CWS) as a sentence-level task, but it suffers from limitations on real-world scenario. In this paper, we address this issue with a context-aware method and optimize the solution at document-level. This paper proposes a three-step strategy to improve the performance for discourse CWS. First, the method utilizes an auxiliary segmenter to remedy the limitation on pre-segmenter. Then the context-aware algorithm computes the confidence of each split. The maximum probability path is reconstructed via this algorithm. Besides, in order to evaluate the performance in discourse, we build a new benchmark consisting of the latest news and Chinese medical articles. Extensive experiments on this benchmark show that our proposed method achieves a competitive performance on a document-level real-world scenario for CWS."
}