Abstract
In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. A major innovation of our tool is the integration of automatic annotation with active learning and proactive learning. This makes the task of creating labeled datasets easier, less time-consuming and requiring less human effort. APLenty is highly flexible and can be adapted to various other tasks.- Anthology ID:
- D18-2019
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Eduardo Blanco, Wei Lu
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 108–113
- Language:
- URL:
- https://aclanthology.org/D18-2019
- DOI:
- 10.18653/v1/D18-2019
- Cite (ACL):
- Minh-Quoc Nghiem and Sophia Ananiadou. 2018. APLenty: annotation tool for creating high-quality datasets using active and proactive learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 108–113, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- APLenty: annotation tool for creating high-quality datasets using active and proactive learning (Nghiem & Ananiadou, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-2019.pdf
- Data
- CoNLL 2003