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    <abstract>In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets. By integrating active learning and proactive learning to the annotation task, Paladin makes the task less time-consuming and requiring less human effort. Although Paladin is designed for multi-label settings, the system is flexible and can be adapted to other tasks in single-label settings.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-demos.28</identifier>
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%0 Conference Proceedings
%T Paladin: an annotation tool based on active and proactive learning
%A Nghiem, Minh-Quoc
%A Baylis, Paul
%A Ananiadou, Sophia
%Y Gkatzia, Dimitra
%Y Seddah, Djamé
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F nghiem-etal-2021-paladin
%X In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets. By integrating active learning and proactive learning to the annotation task, Paladin makes the task less time-consuming and requiring less human effort. Although Paladin is designed for multi-label settings, the system is flexible and can be adapted to other tasks in single-label settings.
%R 10.18653/v1/2021.eacl-demos.28
%U https://aclanthology.org/2021.eacl-demos.28
%U https://doi.org/10.18653/v1/2021.eacl-demos.28
%P 238-243

Markdown (Informal)

[Paladin: an annotation tool based on active and proactive learning](https://aclanthology.org/2021.eacl-demos.28) (Nghiem et al., EACL 2021)

• Paladin: an annotation tool based on active and proactive learning (Nghiem et al., EACL 2021)

ACL

• Minh-Quoc Nghiem, Paul Baylis, and Sophia Ananiadou. 2021. Paladin: an annotation tool based on active and proactive learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 238–243, Online. Association for Computational Linguistics.