@inproceedings{salfinger-etal-2025-la2i2f,
    title = "{LA}{\texttwosuperior}{I}{\texttwosuperior}{F} at {S}em{E}val-2025 Task 5: Reasoning in Embedding Space {--} Fusing Analogical and Ontology-based Reasoning for Document Subject Tagging",
    author = "Salfinger, Andrea  and
      Zaccagna, Luca  and
      Incitti, Francesca  and
      De Nardi, Gianluca  and
      Dal Fabbro, Lorenzo  and
      Snidaro, Lauro",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.314/",
    pages = "2413--2423",
    ISBN = "979-8-89176-273-2",
    abstract = "The LLMs4Subjects shared task invited system contributions that leverage a technical library{'}s tagged document corpus to learn document subject tagging, i.e., proposing adequate subjects given a document{'}s title and abstract. To address the imbalance of this training corpus, team LA{\texttwosuperior}I{\texttwosuperior}F devised a semantic retrieval-based system fusing the results of ontological and analogical reasoning in embedding vector space. Our results outperformed a naive baseline of prompting a llama 3.1-based model, whilst being computationally more efficient and competitive with the state of the art."
}