@inproceedings{perry-2021-lighttag,
    title = "{L}ight{T}ag: Text Annotation Platform",
    author = "Perry, Tal",
    editor = "Adel, Heike  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2021.emnlp-demo.3/",
    doi = "10.18653/v1/2021.emnlp-demo.3",
    pages = "20--27",
    abstract = "Text annotation tools assume that their user`s goal is to create a labeled corpus. However,users view annotation as a necessary evil on the way to deliver business value through NLP.Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle."
}