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
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        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
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        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:

This Egyptian Arabic Wikipedia articles dataset was extracted from the complete Wikipedia dumps of the Egyptian Arabic Wikipedia edition, downloaded on the 1st of January 2024, and processed using the Gensim Python library.

Dataset Features:

We utilized the Wikimedia XTools API to collect the Egyptian Arabic Wikipedia articles’ metadata (dataset features). Specifically, we collected the follwoing metadata/features: total edits, total editors, top editors, total bytes, total characters, total words, creator name, and creation date for each article.

Dataset Subsets:

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.
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.
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}
      }