@inproceedings{bast-etal-2022-elevant,
    title = "{ELEVANT}: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool",
    author = "Bast, Hannah  and
      Hertel, Matthias  and
      Prange, Natalie",
    editor = "Che, Wanxiang  and
      Shutova, Ekaterina",
    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://preview.aclanthology.org/fix-sig-urls/2022.emnlp-demos.8/",
    doi = "10.18653/v1/2022.emnlp-demos.8",
    pages = "72--79",
    abstract = "We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks. Elevant provides an automatic breakdown of the performance by various error categories and by entity type. Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth. A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under \url{https://elevant.cs.uni-freiburg.de} ."
}