@inproceedings{cassotti-etal-2026-senserel,
    title = "{S}ense{R}el: A Sense-Level Benchmark for Denotational and Connotational Meaning Relations",
    author = "Cassotti, Pierluigi  and
      Baes, Naomi  and
      De Pascale, Stefano  and
      de S{\textbackslash}'a, J{\textbackslash}'ader Martins Camboim  and
      Periti, Francesco  and
      Haslam, Nick  and
      Geeraerts, Dirk  and
      Tahmasebi, Nina",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.20/",
    pages = "499--515",
    ISBN = "979-8-89176-390-6",
    abstract = "Polysemy enables a single word to convey multiple related meanings, reflecting the conceptual and emotional aspects of the evolution of the senses. We introduce the first sense-level benchmark, \textit{SenseRel}, for modeling semantic relations between word senses, uniting denotational and connotational aspects of meaning. SenseRel distinguishes denotational relations, such as generalization or metaphor, as well as two connotational dimensions: valence and arousal. We evaluate large language models (LLMs), GPT-4o, Llama 3.1, and DeepSeek, in zero-shot and fine-tuned settings. Results show that GPT-4o best aligns with human affective judgments, while a fine-tuned RoBERTa model excels at classifying denotational relations."
}