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        <title>Extracting First Order Logic formulas from graphical semantic representations</title>
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        <namePart type="given">Rémi</namePart>
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    <abstract>In this paper, we present a method for interpreting Yarn structures as logical formulas in a modal first order logic with temporality. Yarn is a recent semantic formalism that aims to bridge the gap between graph-based and logic-based semantic representations, providing a flexible and expressive framework for capturing the meaning of natural language utterances. Our approach translates the elements of Yarn structures such as predicates, features, into corresponding logical constructs, allowing for an interpretation of the represented meaning. Given that Yarn allows ambiguous representations, we associate to each Yarn structure a set of possible interpretations. We account for a range of semantic phenomena, extending beyond ambiguity to capture aspects of dynamic quantification as well. This work contributes to the understanding of the expressive power of graphical semantic representations and their relationship to formal logic.</abstract>
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%0 Conference Proceedings
%T Extracting First Order Logic formulas from graphical semantic representations
%A de Vergnette, Rémi
%A Tourneur, Vincent
%A Amblard, Maxime
%Y Zhao, Jin
%Y Post, Claire Benet
%Y Hoefer, Elizabeth
%S Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F de-vergnette-etal-2026-extracting
%X In this paper, we present a method for interpreting Yarn structures as logical formulas in a modal first order logic with temporality. Yarn is a recent semantic formalism that aims to bridge the gap between graph-based and logic-based semantic representations, providing a flexible and expressive framework for capturing the meaning of natural language utterances. Our approach translates the elements of Yarn structures such as predicates, features, into corresponding logical constructs, allowing for an interpretation of the represented meaning. Given that Yarn allows ambiguous representations, we associate to each Yarn structure a set of possible interpretations. We account for a range of semantic phenomena, extending beyond ambiguity to capture aspects of dynamic quantification as well. This work contributes to the understanding of the expressive power of graphical semantic representations and their relationship to formal logic.
%R 10.63317/33nasrkkx25h
%U https://aclanthology.org/2026.dmr-1.9/
%U https://doi.org/10.63317/33nasrkkx25h
%P 102-112

Markdown (Informal)

[Extracting First Order Logic formulas from graphical semantic representations](https://aclanthology.org/2026.dmr-1.9/) (de Vergnette et al., DMR 2026)

• Extracting First Order Logic formulas from graphical semantic representations (de Vergnette et al., DMR 2026)

ACL

• Rémi de Vergnette, Vincent Tourneur, and Maxime Amblard. 2026. Extracting First Order Logic formulas from graphical semantic representations. In Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026, pages 102–112, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).