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 "name": "korean_ecmt_raw",
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 "citation": "@inproceedings{nam-etal-2020-effective,\n    title = \"Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction\",\n    author = \"Nam, Sangha  and\n      Lee, Minho  and\n      Kim, Donghwan  and\n      Han, Kijong  and\n      Kim, Kuntae  and\n      Yoon, Sooji  and\n      Kim, Eun-kyung  and\n      Choi, Key-Sun\",\n    editor = \"Calzolari, Nicoletta  and\n      B{\\'e}chet, Fr{\\'e}d{\\'e}ric  and\n      Blache, Philippe  and\n      Choukri, Khalid  and\n      Cieri, Christopher  and\n      Declerck, Thierry  and\n      Goggi, Sara  and\n      Isahara, Hitoshi  and\n      Maegaard, Bente  and\n      Mariani, Joseph  and\n      Mazo, H{\\'e}l{\\`e}ne  and\n      Moreno, Asuncion  and\n      Odijk, Jan  and\n      Piperidis, Stelios\",\n    booktitle = \"Proceedings of the Twelfth Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://aclanthology.org/2020.lrec-1.27\",\n    pages = \"212--219\",\n    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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 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
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