@inproceedings{nachtegael-etal-2023-alambic,
    title = "{ALAMBIC} : Active Learning Automation Methods to Battle Inefficient Curation",
    author = "Nachtegael, Charlotte  and
      De Stefani, Jacopo  and
      Lenaerts, Tom",
    editor = "Croce, Danilo  and
      Soldaini, Luca",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.eacl-demo.14/",
    doi = "10.18653/v1/2023.eacl-demo.14",
    pages = "117--127",
    abstract = "In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification task. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of model, features and active learning strategies. The code is available on \url{https://github.com/Trusted-AI-Labs/ALAMBIC} and a video demonstration is available on \url{https://youtu.be/4oh8UADfEmY}."
}