APLenty: annotation tool for creating high-quality datasets using active and proactive learning

Minh-Quoc Nghiem, Sophia Ananiadou


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D18-2019.pdf
Data
CoNLL-2003