README.md

metadata

language:
  - ar
license: mit
size_categories:
  - 100K<n<1M
task_categories:
  - text-classification
pretty_name: Detect-Egyptian-Wikipedia-Articles
configs:
  - config_name: balanced
    data_files:
      - split: train
        path: balanced/train-*
      - split: test
        path: balanced/test-*
  - config_name: unbalanced
    data_files:
      - split: train
        path: unbalanced/train-*
      - split: test
        path: unbalanced/test-*
  - config_name: uncategorized
    data_files:
      - split: train
        path: uncategorized/train-*
      - split: test
        path: uncategorized/test-*
dataset_info:
  - config_name: balanced
    features:
      - name: page_title
        dtype: string
      - name: creation_date
        dtype: string
      - name: creator_name
        dtype: string
      - name: total_edits
        dtype: int64
      - name: total_editors
        dtype: int64
      - name: top_editors
        dtype: string
      - name: bots_editors_percentage
        dtype: float64
      - name: humans_editors_percentage
        dtype: float64
      - name: total_bytes
        dtype: int64
      - name: total_chars
        dtype: int64
      - name: total_words
        dtype: int64
      - name: page_text
        dtype: string
      - name: label
        dtype: string
    splits:
      - name: train
        num_bytes: 32565713
        num_examples: 16000
      - name: test
        num_bytes: 8243228
        num_examples: 4000
    download_size: 18217654
    dataset_size: 40808941
  - config_name: unbalanced
    features:
      - name: page_title
        dtype: string
      - name: creation_date
        dtype: string
      - name: creator_name
        dtype: string
      - name: total_edits
        dtype: int64
      - name: total_editors
        dtype: int64
      - name: top_editors
        dtype: string
      - name: bots_editors_percentage
        dtype: float64
      - name: humans_editors_percentage
        dtype: float64
      - name: total_bytes
        dtype: int64
      - name: total_chars
        dtype: int64
      - name: total_words
        dtype: int64
      - name: page_text
        dtype: string
      - name: label
        dtype: string
    splits:
      - name: train
        num_bytes: 132509046
        num_examples: 133120
      - name: test
        num_bytes: 33292670
        num_examples: 33281
    download_size: 59449711
    dataset_size: 165801716
  - config_name: uncategorized
    features:
      - name: page_title
        dtype: string
      - name: creation_date
        dtype: string
      - name: creator_name
        dtype: string
      - name: total_edits
        dtype: int64
      - name: total_editors
        dtype: int64
      - name: top_editors
        dtype: string
      - name: bots_editors_percentage
        dtype: float64
      - name: humans_editors_percentage
        dtype: float64
      - name: total_bytes
        dtype: int64
      - name: total_chars
        dtype: int64
      - name: total_words
        dtype: int64
      - name: page_text
        dtype: string
      - name: label
        dtype: string
    splits:
      - name: train
        num_bytes: 607754601
        num_examples: 455411
      - name: test
        num_bytes: 151613029
        num_examples: 113853
    download_size: 141377798
    dataset_size: 759367630
source_datasets:
  - Egyptian Wikipedia
tags:
  - Wikipedia

Detect Egyptian Wikipedia Template-translated Articles

Dataset Description:

We release the heuristically filtered, manually processed, and automatically classified Egyptian Arabic Wikipedia articles dataset. This dataset was used to develop a web-based detection system to automatically identify the template-translated articles on the Egyptian Arabic Wikipedia edition. The system is called Egyptian Arabic Wikipedia Scanner and is hosted on Hugging Face Spaces, here: SaiedAlshahrani/Detect-Egyptian-Wikipedia-Articles.

This dataset is introduced in a research paper titled "Leveraging Corpus Metadata to Detect Template-based Translation: An Exploratory Case Study of the Egyptian Arabic Wikipedia Edition", which is accepted at LREC-COLING 2024: The 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6), and is currently released under an MIT license.

Dataset Sources:

Dataset Features:

Dataset Subsets:

1. Balanced: A balanced subset of the dataset comprised 20K (10K for each class) and was split in the ratio of 80:20 for training and testing. This subset was filtered and processed using selected heuristic rules.
2. Unbalanced: An unbalanced subset of the dataset comprised 166K and was split in the ratio of 80:20 for training and testing. This subset is the rest of the filtered and processed articles using selected heuristic rules.
3. Uncategorized: Another unbalanced subset of the dataset comprised 569K and was split in the ratio of 80:20 for training and testing, but this was classified automatically using the XGBoost classifier trained using the balanced subset.

Citations:

Please cite our paper if you have used our dataset in any way 😊

Short Citation:

Saied Alshahrani, Hesham Haroon, Ali Elfilali, Mariama Njie, and Jeanna Matthews. 2024. Leveraging Corpus Metadata to Detect Template-based Translation: An Exploratory Case Study of the Egyptian Arabic Wikipedia Edition. arXiv preprint arXiv:2404.00565.

BibTeX Citation:

@article{alshahrani2024leveraging,
      title={Leveraging Corpus Metadata to Detect Template-based Translation: An Exploratory Case Study of the Egyptian Arabic Wikipedia Edition}, 
      author={Saied Alshahrani and Hesham Haroon and Ali Elfilali and Mariama Njie and Jeanna Matthews},
      year={2024},
      eprint={2404.00565},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
      journal={arXiv preprint arXiv:2404.00565},
      url={https://arxiv.org/abs/2404.00565}
      }