@inproceedings{qiao-etal-2024-quantum,
    title = "A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection",
    author = "Qiao, Wenbo  and
      Zhang, Peng  and
      Ma, ZengLai",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.127",
    pages = "1435--1445",
    abstract = "Enabling machines with the capability to recognize and comprehend metaphors is a crucial step toward achieving artificial intelligence. In linguistic theories, metaphor can be identified through Metaphor Identification Procedure (MIP) or Selectional Preference Violation (SPV), both of which are typically considered as matching tasks in the field of natural language processing. However, the implementation of MIP poses a challenge due to the semantic uncertainty and ambiguity of literal meanings of words. Simultaneously, SPV often struggles to recognize conventional metaphors. Inspired by Quantum Language Model (QLM) for modeling semantic uncertainty and fine-grained feature matching, we propose a quantum-inspired matching network for metaphor detection. Specifically, we use the density matrix to explicitly characterize the literal meanings of the target word for MIP, in order to model the uncertainty and ambiguity of the literal meanings of words. This can make SPV effective even in the face of conventional metaphors. MIP and SPV are then achieved by fine-grained feature matching. The results of the experiment finally demonstrated our approach has strong competitiveness.",
}

Enabling machines with the capability to recognize and comprehend metaphors is a crucial step toward achieving artificial intelligence. In linguistic theories, metaphor can be identified through Metaphor Identification Procedure (MIP) or Selectional Preference Violation (SPV), both of which are typically considered as matching tasks in the field of natural language processing. However, the implementation of MIP poses a challenge due to the semantic uncertainty and ambiguity of literal meanings of words. Simultaneously, SPV often struggles to recognize conventional metaphors. Inspired by Quantum Language Model (QLM) for modeling semantic uncertainty and fine-grained feature matching, we propose a quantum-inspired matching network for metaphor detection. Specifically, we use the density matrix to explicitly characterize the literal meanings of the target word for MIP, in order to model the uncertainty and ambiguity of the literal meanings of words. This can make SPV effective even in the face of conventional metaphors. MIP and SPV are then achieved by fine-grained feature matching. The results of the experiment finally demonstrated our approach has strong competitiveness.
Qiao, Wenbo Zhang, Peng Ma, ZengLai
A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection