@inproceedings{shnarch-etal-2022-label,
    title = "Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours",
    author = "Shnarch, Eyal  and
      Halfon, Alon  and
      Gera, Ariel  and
      Danilevsky, Marina  and
      Katsis, Yannis  and
      Choshen, Leshem  and
      Santillan Cooper, Martin  and
      Epelboim, Dina  and
      Zhang, Zheng  and
      Wang, Dakuo",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-demos.16",
    pages = "159--168",
    abstract = "Label Sleuth is an open source platform for building text classifiers which does not require coding skills nor machine learning knowledge.- Project website: [https://www.label-sleuth.org/](https://www.label-sleuth.org/)- Link to screencast video: [https://vimeo.com/735675461](https://vimeo.com/735675461){\#}{\#}{\#} AbstractText classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier we introduce *Label Sleuth*, a free open source system for labeling and creating text classifiers. This system is unique for: - being a no-code system, making NLP accessible for non-experts. - guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours. - being open for configuration and extension by developers.By open sourcing Label Sleuth we hope to build a community of users and developers that will widen the utilization of NLP models.",
}