APLenty (Active Proactive Learning System) is an annotation tool for creating high-quality sequence labelling datasets using active and proactive learning. A major innovation of the tool is the integration of automatic annotation with active learning and proactive learning. This makes the task of creating labelled datasets easier and less time-consuming, requiring less human effort. APLenty is highly flexible and can be adapted to various tasks.


Obtaining labeled data is difficult, time-consuming, and require a lot of human effort. Many libraries and systems focus on active learning. However, little attention has been paid to the interaction between the annotators and the active learning algorithm. APLenty combines a well-known annotation tool (brat) with active/proactive learning.


  • Proactive learning integration - APLenty makes annotation easy and time-efficient, and requires less human effort by offering automatic and proactive learning.
  • An easy-to-use interface for annotators - APLenty adapts the interface of the brat rapid annotation tool, making annotation intuitive and easy to use.
  • Suitable for sequence labelling - APLenty is best used for sequence labelling tasks, although it can be used for other classification problems


  • Manager creates a project; uploads the training, test, and unlabelled data; defines the tagset; chooses the active/proactive learning strategy.
  • Annotator selects a span of text on the displayed sentence and chooses a tag for that span.
  • APLenty triggers the training process with newly annotated data and updates the sentences for the next annotation batch.



A demo version of APLenty is available here.


Nghiem, MQ. and Ananiadou, S. (2018). APLenty: annotation tool for creating high-quality datasets using active and proactive learning. In: Proceedings of Empirical Methods in Natural Language Processing (System Demonstrations), pp. 108 - 113.

Li, M., Myrman, A. F., Mu, T. and Ananiadou, S. (2019). Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 2873-2883

Li, M., Nguyen, N. T. H. and Ananiadou, S. (2017). Proactive Learning for Named Entity Recognition. In: Proceedings of BioNLP 2017, pp. 117-125, Association for Computational Linguistics


To obtain further information about APLenty, please contact Prof. Sophia Ananiadou.