@inproceedings{nghiem-ananiadou-2018-aplenty,
    title = "{APL}enty: annotation tool for creating high-quality datasets using active and proactive learning",
    author = "Nghiem, Minh-Quoc  and
      Ananiadou, Sophia",
    editor = "Blanco, Eduardo  and
      Lu, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D18-2019/",
    doi = "10.18653/v1/D18-2019",
    pages = "108--113",
    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."
}