@inproceedings{gedi-etal-2026-morphologically,
    title = "Morphologically-informed {S}omali Lemmatization Corpus built with a Web-based Crowdsourcing Platform",
    author = "Gedi, Abdifatah Ahmed  and
      Mohamed, Shafie Abdi  and
      Yusuf, Yusuf A.  and
      Mohamed, Muhidin A.  and
      Hassan, Fuad Mire  and
      Assowe, Houssein A",
    editor = "Chimoto, Everlyn Asiko  and
      Lignos, Constantine  and
      Muhammad, Shamsuddeen  and
      Abdulmumin, Idris  and
      Siro, Clemencia  and
      Adelani, David Ifeoluwa",
    booktitle = "Proceedings of the 7th Workshop on {A}frican Natural Language Processing ({A}frica{NLP} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.africanlp-main.17/",
    pages = "179--189",
    ISBN = "979-8-89176-364-7",
    abstract = "Lemmatization, which reduces words to their root forms, plays a key role in tasks such as information retrieval, text indexing, and machinelearning-based language models. However, a key research challenge for low-resourced languages such as the Somali is the lack of human-annotated lemmatization datasets and reliable ground truth to underpin accurate morphological analysis and training relevant NLP models. To address this problem, we developed the first large-scale, purpose-built Somali lemmatization lexicon, coupled with a crowdsourcing platform for ongoing expansion. The system leverages Somali{'}s agglutinative and derivational morphology, encompassing over5,584 root words and 78,629 derivative forms, each annotated with part-of-speech tags. For data validation purpose, we have devised a pilot lexicon-based lemmatizer integrated with rule-based logic to handle out-of-vocabulary terms. Evaluation on a 294-document corpuscovering news articles, social media posts, and short messages shows lemmatization accuracies of 51.27{\%} for full articles, 44.14{\%} forexcerpts, and 59.51{\%} for short texts such as tweets. These results demonstrate that combining lexical resources, POS tagging, and rulebased strategies provides a robust and scalable framework for addressing morphological complexity in Somali and other low-resource languages"
}