@inproceedings{zhang-etal-2021-croano,
    title = "{C}ro{A}no : A Crowd Annotation Platform for Improving Label Consistency of {C}hinese {NER} Dataset",
    author = "Zhang, Baoli  and
      Li, Zhucong  and
      Gan, Zhen  and
      Chen, Yubo  and
      Wan, Jing  and
      Liu, Kang  and
      Zhao, Jun  and
      Liu, Shengping  and
      Shi, Yafei",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-demo.32",
    doi = "10.18653/v1/2021.emnlp-demo.32",
    pages = "275--282",
    abstract = "In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96{\%} and +2.57{\%} F1 respectively in model performance.",
}