@inproceedings{lechelle-etal-2019-wire57,
    title = "{W}i{R}e57 : A Fine-Grained Benchmark for Open Information Extraction",
    author = "Lechelle, William  and
      Gotti, Fabrizio  and
      Langlais, Phillippe",
    editor = "Friedrich, Annemarie  and
      Zeyrek, Deniz  and
      Hoek, Jet",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-4002",
    doi = "10.18653/v1/W19-4002",
    pages = "6--15",
    abstract = "We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including coreference and granularity, and we take steps toward addressing inference, a significant problem. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.",
}