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U4RASD/ArSRED

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Permissive or share-alike

The declared licence (cc-by-sa-4.0) is classified as share-alike.

Declared licence: cc-by-sa-4.0, from the tagEvidence: README.md at 4b40e45
Licence files: No licence file at the top level of the repositoryEvidence: file list at 4b40e45
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

token-classification

Citation text from the card

@inproceedings{mraikhat-etal-2024-areej,
    title = "{AREE}j: {A}rabic Relation Extraction with Evidence",
    author = "Rakan Al Mraikhat, Osama  and
      Hamoud, Hadi  and
      Zaraket, Fadi A.",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Eskander, Ramy  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Abdelali, Ahmed  and
      Touileb, Samia  and
      Hamed, Injy  and
      Onaizan, Yaser  and
      Alhafni, Bashar  and
      Antoun, Wissam  and
      Khalifa, Salam  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Mrini, Khalil",
    booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.arabicnlp-1.6/",
    doi = "10.18653/v1/2024.arabicnlp-1.6",
    pages = "67--72",
    abstract = "Relational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and atype. In this paper, we consider Arabic text and introduce evidence enrichment which intuitivelyinforms models for better predictions. Relational evidence is an expression in the textthat explains how sources and targets relate. {\%}It also provides hints from which models learn. This paper augments the existing relational extraction dataset with evidence annotation to its 2.

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dataset_licence({ "id": "U4RASD/ArSRED" })
signal: "permissive"
stated_licence: "cc-by-sa-4.0"
sources: 0