Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora

Berkay Bas; Jelke Bloem; Xiaojuan Tan

doi:10.63317/2zr7e2gvwuo9

Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora

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Abstract

Although recent advances have focused on detecting metaphors, existing models generally treat them as static entities. There has been little research into identifying dynamic metaphors in discourse. This article addresses this gap by focusing on metaphoricity cues: Linguistic signals that may indicate the activation of metaphoric meaning in different discourse contexts. This study examines the ability of OpenAI’s O-series models (O4-mini, O4-mini-high and O3) in detecting five metaphoricity cues in the U.S. trade discourse, including cues of explicit mapping, emphasis, marking, repetition and novelisation. Research results show that the models performed best on repetition and emphasis, while novelisation was the most difficult cue to detect.

Anthology ID:: 2026.lrec-1.357
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 4547–4559
Language:
External URL:: https://lrec.elra.info/lrec2026-main-357
DOI:: 10.63317/2zr7e2gvwuo9
Bibkey:
Cite (ACL):: Berkay Bas, Jelke Bloem, and Xiaojuan Tan. 2026. Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4547–4559, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora (Bas et al., LREC 2026)
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@inproceedings{bas-etal-2026-towards,
    title = "Towards Dynamic Metaphor Identification: Evaluating {GPT} {O}-Series Models on Five Metaphoricity Cues in {U}.{S}. Trade Corpora",
    author = "Bas, Berkay  and
      Bloem, Jelke  and
      Tan, Xiaojuan",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.357/",
    doi = "10.63317/2zr7e2gvwuo9",
    pages = "4547--4559",
    abstract = "Although recent advances have focused on detecting metaphors, existing models generally treat them as static entities. There has been little research into identifying dynamic metaphors in discourse. This article addresses this gap by focusing on metaphoricity cues: Linguistic signals that may indicate the activation of metaphoric meaning in different discourse contexts. This study examines the ability of OpenAI{'}s O-series models (O4-mini, O4-mini-high and O3) in detecting five metaphoricity cues in the U.S. trade discourse, including cues of explicit mapping, emphasis, marking, repetition and novelisation. Research results show that the models performed best on repetition and emphasis, while novelisation was the most difficult cue to detect."
}

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%0 Conference Proceedings
%T Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora
%A Bas, Berkay
%A Bloem, Jelke
%A Tan, Xiaojuan
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F bas-etal-2026-towards
%X Although recent advances have focused on detecting metaphors, existing models generally treat them as static entities. There has been little research into identifying dynamic metaphors in discourse. This article addresses this gap by focusing on metaphoricity cues: Linguistic signals that may indicate the activation of metaphoric meaning in different discourse contexts. This study examines the ability of OpenAI’s O-series models (O4-mini, O4-mini-high and O3) in detecting five metaphoricity cues in the U.S. trade discourse, including cues of explicit mapping, emphasis, marking, repetition and novelisation. Research results show that the models performed best on repetition and emphasis, while novelisation was the most difficult cue to detect.
%R 10.63317/2zr7e2gvwuo9
%U https://aclanthology.org/2026.lrec-1.357/
%U https://doi.org/10.63317/2zr7e2gvwuo9
%P 4547-4559

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Markdown (Informal)

[Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora](https://aclanthology.org/2026.lrec-1.357/) (Bas et al., LREC 2026)

Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora (Bas et al., LREC 2026)

ACL

Berkay Bas, Jelke Bloem, and Xiaojuan Tan. 2026. Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4547–4559, Palma de Mallorca, Spain. ELRA Language Resource Association.