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ml-resources/CMNEE

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 3819044
Licence files: LICENSE, detected as apache-2.0Evidence: LICENSE at 3819044
Declared sources: None listed in the card metadata
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token-classification

Citation text from the card

@inproceedings{zhu-etal-2024-cmnee-large,
    title = "{CMNEE}:A Large-Scale Document-Level Event Extraction Dataset Based on Open-Source {C}hinese Military News",
    author = "Zhu, Mengna  and
      Xu, Zijie  and
      Zeng, Kaisheng  and
      Xiao, Kaiming  and
      Wang, Mao  and
      Ke, Wenjun  and
      Huang, Hongbin",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italy",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.299",
    pages = "3367--3379",
    abstract = "Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 even

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dataset_licence({ "id": "ml-resources/CMNEE" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0