@inproceedings{al-omar-etal-2022-establishing,
    title = "Establishing a Baseline for {A}rabic Patents Classification: A Comparison of Twelve Approaches",
    author = "Al-Omar, Taif Omar  and
      Al-Khalifa, Hend  and
      Al-Matham, Rawan",
    editor = "Bouamor, Houda  and
      Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Rambow, Owen  and
      Bougares, Fethi  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Khalifa, Salam  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.wanlp-1.26/",
    doi = "10.18653/v1/2022.wanlp-1.26",
    pages = "287--294",
    abstract = "Nowadays, the number of patent applications is constantly growing and there is an economical interest on developing accurate and fast models to automate their classification task. In this paper, we introduce the first public Arabic patent dataset called ArPatent and experiment with twelve classification approaches to develop a baseline for Arabic patents classification. To achieve the goal of finding the best baseline for classifying Arabic patents, different machine learning, pre-trained language models as well as ensemble approaches were conducted. From the obtained results, we can observe that the best performing model for classifying Arabic patents was ARBERT with F1 of 66.53{\%}, while the ensemble approach of the best three performing language models, namely: ARBERT, CAMeL-MSA, and QARiB, achieved the second best F1 score, i.e., 64.52{\%}."
}