--- title: "Extracting First Order Logic formulas from — Vergnette 2026" description: "Graph-based meaning representations such as amr are easy to annotate and parse, but they leave out logically important information like quantifier scope." authors: ["Rémi de Vergnette", "Vincent Tourneur", "Maxime Amblard"] year: 2026 venue: "Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026" source: "https://aclanthology.org/2026.dmr-1.9/" doi: "10.63317/33nasrkkx25h" licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)" licence_url: "https://creativecommons.org/licenses/by-nc/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/vergnette-2026-extracting-first-order-logic-formulas/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/vergnette-2026-extracting-first-order-logic-formulas/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/6/) / Extracting First Order Logic formulas from graphical semantic representations # Extracting First Order Logic formulas from graphical semantic representations **Authors:** [Rémi de Vergnette](https://md.research.elamur.ai/free/en/authors/remi-de-vergnette/), Vincent Tourneur, [Maxime Amblard](https://md.research.elamur.ai/free/en/authors/maxime-amblard/) · **Institutions:** [Centre National de la Recherche Scientifique](https://md.research.elamur.ai/free/en/labs/centre-national-de-la-recherche-scientifique/) · [Institut national de recherche en sciences et technologies du numérique](https://md.research.elamur.ai/free/en/labs/institut-national-de-recherche-en-sciences-et-technologies-du-numerique/) · [Laboratoire Lorrain de Recherche en Informatique et ses Applications](https://md.research.elamur.ai/free/en/labs/laboratoire-lorrain-de-recherche-en-informatique-et-ses-applications/) · [Université de Lorraine](https://md.research.elamur.ai/free/en/universities/universite-de-lorraine/) · **Year:** 2026 · **Venue:** Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026 · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.dmr-1.9/) · [PDF](https://aclanthology.org/2026.dmr-1.9.pdf) · [DOI 10.63317/33nasrkkx25h](https://doi.org/10.63317/33nasrkkx25h) · [OpenAlex](https://openalex.org/W7166536676) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary Graph-based meaning representations such as amr are easy to annotate and parse, but they leave out logically important information like quantifier scope. Logic-based formalisms capture that information but are costly to produce. Yarn, a layered formalism, tries to combine the strengths of both. This paper asks how expressive Yarn really is by giving its structures a formal logical interpretation. The authors define a translation in three steps: - A Yarn structure becomes an intermediate pair (F, R). F is a labelled forest recording scope dependencies, and R holds flat predicate-argument relations. - A constraint system C(F, R) is built. Its solutions are the admissible scope trees, so Yarn's underspecification is spelled out explicitly. - A compositional function turns each admissible tree into a formula of modal first-order logic with temporality. Because scope can stay underspecified, one Yarn graph maps to a set of formulas rather than a single one. Worked examples show that the approach handles interacting scopes and dynamic quantification. This grounds Yarn in formal semantics and positions it alongside constraint-based frameworks such as MRS and Predicate Logic Unplugged. ## Key points - Formally, Yarn (Pavlova et al., 2024; Pavlova, 2025) is defined as a tuple with nine components, which group by role as follows (with I as the ninth): - V and E form an amr-like predicate graph built on PropBank frames. - F, L and H encode features and their scope chains. - S, D and C carry discourse structure. - The study restricts features to four layers: tense, modality, negation and quantification. I edges, which express set membership, are simply treated as E edges. - The running example is "In the future, politicians won't like researchers who signed a petition." It shows that ordering future, necessity and negation differently gives distinct readings, either an impossible event or one that might not happen. - The pipeline is Yarn → (F, R) → constraint system C(F, R) → admissible trees → modal temporal FOL formulas. - The work is purely theoretical, with no corpus or parser evaluation. It continues earlier efforts to give logical semantics to amr (Bos, 2016; Lai et al., 2020), and its scope-constraint layer resembles Bos (1996). *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{vergnette2026extracting, title = {{Extracting First Order Logic formulas from graphical semantic representations}}, author = {Vergnette, Rémi de and Tourneur, Vincent and Amblard, Maxime}, booktitle = {Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026}, year = {2026}, doi = {10.63317/33nasrkkx25h}, url = {https://doi.org/10.63317/33nasrkkx25h} } ``` - [← previous Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/smid-2026-extending-czech-aspect-based-sentiment/) - [next → Extracting Medical Image-Related Entities from Spanish Electronic Health Records Using NER Methods](https://md.research.elamur.ai/free/en/papers/platas-2026-extracting-medical-image-related-entities/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/6/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. James F. Allen. 1983. Maintaining knowledge about temporal intervals. Commun. ACM, 26:832–843. *Figure 10: The only possible tree obtained by merging the forest in Figure 9* [find](https://scholar.google.com/scholar?q=James+F%2E+Allen%2E+1983%2E+Maintaining+knowledge+about+temporal+intervals%2E+Commun%2E+ACM%2C+26%3A832%E2%80%93843%2E+%2AFigure+10%3A+The+only+possible+tree+obtained+by+merging+the+forest+in+Figure+9%2A)