@inproceedings{kruse-ahmed-2024-tafsirextractor,
    title = "{T}afsir{E}xtractor: Text Preprocessing Pipeline preparing Classical {A}rabic Literature for Machine Learning Applications",
    author = "Kruse, Carl  and
      Ahmed, Sajawel",
    editor = "Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Mubarak, Hamdy  and
      Ali, Mona  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.osact-1.8",
    pages = "67--73",
    abstract = "In this paper, we present a comprehensive tool of preprocessing Classical Arabic (CA) literature in the field of historical exegetical studies for machine learning (ML) evaluations. Most recent ML models require the training data to be in a specific format (e.g. XML, TEI, CoNLL) to use it afterwards for ML applications such as Named Entity Recognition (NER) or Topic Modeling (TM). We report on how our method works and can be applied by other researchers with similar endeavors. Thereby, the importance of this comprehensive tool of preprocessing is demonstrated, as this novel approach has no predecessors for CA yet. We achieve results that enable the training of current ML models leading to state-of-the art performance for NER and TM on CA literature. We make our tool along its source code and data freely available for the Natural Language Processing (NLP) research community.",
}

In this paper, we present a comprehensive tool of preprocessing Classical Arabic (CA) literature in the field of historical exegetical studies for machine learning (ML) evaluations. Most recent ML models require the training data to be in a specific format (e.g. XML, TEI, CoNLL) to use it afterwards for ML applications such as Named Entity Recognition (NER) or Topic Modeling (TM). We report on how our method works and can be applied by other researchers with similar endeavors. Thereby, the importance of this comprehensive tool of preprocessing is demonstrated, as this novel approach has no predecessors for CA yet. We achieve results that enable the training of current ML models leading to state-of-the art performance for NER and TM on CA literature. We make our tool along its source code and data freely available for the Natural Language Processing (NLP) research community.
Kruse, Carl Ahmed, Sajawel
TafsirExtractor: Text Preprocessing Pipeline preparing Classical Arabic Literature for Machine Learning Applications