@inproceedings{datseris-etal-2025-end,
    title = "End-to-End Deep Learning for Named Entity Recognition and Relation Extraction in Gut-Brain Axis {P}ub{M}ed Abstracts",
    author = "Datseris, Aleksis Ioannis  and
      Kuzmanov, Mario  and
      Nikolova-Koleva, Ivelina  and
      Taskov, Dimitar  and
      Boytcheva, Svetla",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.31/",
    pages = "254--259",
    abstract = "This is a comparative study tackling named entity recognition and relation extraction from PubMed abstracts with focus on the gut-brain interplay. The proposed systems for named entity recognition cover a range of models and techniques from traditional gazetteer-based approaches, transformer-based approaches, transformer domain adaptation, large models pre-training as well as LLM prompting. The best performing model among these achieves 82.53{\%} F1-score. The relation extraction task is addressed with ATLOP and LLMs and their best results reach F1 up to 63.80{\%} on binary relation extraction, 89.40{\%} on ternary tag-based relation extraction and 40.32{\%} on ternary mention-based relation extraction."
}