coref-data/korean_ecmt_raw

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The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-sa-4.0, from the tagEvidence: README.md at 7a45a48
Licence files: No licence file at the top level of the repositoryEvidence: file list at 7a45a48
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@inproceedings{nam-etal-2020-effective,
    title = "Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction",
    author = "Nam, Sangha  and
      Lee, Minho  and
      Kim, Donghwan  and
      Han, Kijong  and
      Kim, Kuntae  and
      Yoon, Sooji  and
      Kim, Eun-kyung  and
      Choi, Key-Sun",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.27",
    pages = "212--219",
    abstract = "Information extraction from unstructured texts plays a vital role in the field of natural language processing. Although there has been extensive research into each information extraction task (i.e., entity linking, coreference resolution, and relation extraction), data are not available for a continuous and coherent evaluation of all information extraction tasks in a comprehensive framework. Given that each task is performed and evaluated with

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dataset_licence({ "id": "coref-data/korean_ecmt_raw" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0