@inproceedings{ueda-etal-2023-kwja,
    title = "{KWJA}: A Unified {J}apanese Analyzer Based on Foundation Models",
    author = "Ueda, Nobuhiro  and
      Omura, Kazumasa  and
      Kodama, Takashi  and
      Kiyomaru, Hirokazu  and
      Murawaki, Yugo  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.52",
    doi = "10.18653/v1/2023.acl-demo.52",
    pages = "538--548",
    abstract = "We present KWJA, a high-performance unified Japanese text analyzer based on foundation models.KWJA supports a wide range of tasks, including typo correction, word segmentation, word normalization, morphological analysis, named entity recognition, linguistic feature tagging, dependency parsing, PAS analysis, bridging reference resolution, coreference resolution, and discourse relation analysis, making it the most versatile among existing Japanese text analyzers.KWJA solves these tasks in a multi-task manner but still achieves competitive or better performance compared to existing analyzers specialized for each task.KWJA is publicly available under the MIT license at \url{https://github.com/ku-nlp/kwja}.",
}

We present KWJA, a high-performance unified Japanese text analyzer based on foundation models.KWJA supports a wide range of tasks, including typo correction, word segmentation, word normalization, morphological analysis, named entity recognition, linguistic feature tagging, dependency parsing, PAS analysis, bridging reference resolution, coreference resolution, and discourse relation analysis, making it the most versatile among existing Japanese text analyzers.KWJA solves these tasks in a multi-task manner but still achieves competitive or better performance compared to existing analyzers specialized for each task.KWJA is publicly available under the MIT license at \url{https://github.com/ku-nlp/kwja}.
Ueda, Nobuhiro Omura, Kazumasa Kodama, Takashi Kiyomaru, Hirokazu Murawaki, Yugo Kawahara, Daisuke Kurohashi, Sadao
KWJA: A Unified Japanese Analyzer Based on Foundation Models