@inproceedings{lin-etal-2022-dotat,
    title = "{D}o{TAT}: A Domain-oriented Text Annotation Tool",
    author = "Lin, Yupian  and
      Ruan, Tong  and
      Liang, Ming  and
      Cai, Tingting  and
      Du, Wen  and
      Wang, Yi",
    editor = "Basile, Valerio  and
      Kozareva, Zornitsa  and
      Stajner, Sanja",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-demo.1",
    doi = "10.18653/v1/2022.acl-demo.1",
    pages = "1--8",
    abstract = "We propose DoTAT, a domain-oriented text annotation tool. The tool designs and implements functions heavily in need in domain-oriented information extraction. Firstly, the tool supports a multi-person collaborative process with automatically merging and review, which can greatly improve the annotation accuracy. Secondly, the tool provides annotation of events, nested event and nested entity, which are frequently required in domain-related text structuring tasks. Finally, DoTAT provides visual annotation specification definition, automatic batch annotation and iterative annotation to improve annotation efficiency. Experiments on the ACE2005 dataset show that DoTAT can reduce the event annotation time by 19.7{\%} compared with existing annotation tools. The accuracy without review is 84.09{\%}, 1.35{\%} higher than Brat and 2.59{\%} higher than Webanno. The accuracy of DoTAT even reaches 93.76{\%} with review. The demonstration video can be accessed from \url{https://ecust-nlp-docker.oss-cn-shanghai.aliyuncs.com/dotat_demo.mp4}. A live demo website is available at \url{https://github.com/FXLP/MarkTool}.",
}