metadata

license: cc-by-sa-4.0
task_categories:
  - token-classification
language:
  - ar
size_categories:
  - 100K<n<1M

AREEj: Arabic Relation Extraction with Evidence

This dataset was made by adding evidence annotations to the Arabic subset of SRED^FM. The dataset is from the Proceedings of The Second Arabic Natural Language Processing Conference paper AREEj: Arabic Relation Extraction with Evidence. If you use the dataset or the model, please reference this work in your paper:

@inproceedings{mraikhat-etal-2024-areej,
    title = "{AREE}j: {A}rabic Relation Extraction with Evidence",
    author = "Mraikhat, Osama  and
      Hamoud, Hadi  and
      Zaraket, Fadi",
    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",
    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 2.9-million Arabic relations.We leverage the augmented dataset to build , a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall).",
}

License

ArSRED is licensed under the CC BY-SA 4.0 license. The text of the license can be found here.