|
--- |
|
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<sup>FM</sup>. The dataset is from the Proceedings of The Second Arabic Natural Language Processing Conference paper [AREEj: Arabic Relation Extraction with Evidence](https://aclanthology.org/2024.arabicnlp-1.6/). 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](https://creativecommons.org/licenses/by-sa/4.0/). |
