@inproceedings{munne-etal-2025-zero,
    title = "Zero-Shot Entailment Learning for Ontology-Based Biomedical Annotation Without Explicit Mentions",
    author = "Munne, Rumana Ferdous  and
      Nishida, Noriki  and
      Liu, Shanshan  and
      Tokunaga, Narumi  and
      Yamagata, Yuki  and
      Kozaki, Kouji  and
      Matsumoto, Yuji",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2025.coling-main.542/",
    pages = "8148--8159",
    abstract = "Automatic biomedical annotation is essential for advancing medical research, diagnosis, and treatment. However, it presents significant challenges, especially when entities are not explicitly mentioned in the text, leading to difficulties in extraction of relevant information. These challenges are intensified by unclear terminology, implicit background knowledge, and the lack of labeled training data. Annotating with a specific ontology adds another layer of complexity, as it requires aligning text with a predefined set of concepts and relationships. Manual annotation is time-consuming and expensive, highlighting the need for automated systems to handle large volumes of biomedical data efficiently. In this paper, we propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. Our method reformulates the annotation task as a multi-class, multi-label classification problem and uses natural language inference to classify text into related HOIP processes. Experimental results show promising performance, especially when processes are not explicitly mentioned, highlighting the effectiveness of our approach for ontological annotation of biomedical literature."
}