DoTAT: A Domain-oriented Text Annotation Tool

Yupian Lin, Tong Ruan, Ming Liang, Tingting Cai, Wen Du, Yi Wang


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 https://ecust-nlp-docker.oss-cn-shanghai.aliyuncs.com/dotat_demo.mp4. A live demo website is available at https://github.com/FXLP/MarkTool.
Anthology ID:
2022.acl-demo.1
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Valerio Basile, Zornitsa Kozareva, Sanja Stajner
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–8
Language:
URL:
https://aclanthology.org/2022.acl-demo.1
DOI:
10.18653/v1/2022.acl-demo.1
Bibkey:
Cite (ACL):
Yupian Lin, Tong Ruan, Ming Liang, Tingting Cai, Wen Du, and Yi Wang. 2022. DoTAT: A Domain-oriented Text Annotation Tool. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 1–8, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
DoTAT: A Domain-oriented Text Annotation Tool (Lin et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.acl-demo.1.pdf
Code
 fxlp/marktool