@inproceedings{ngo-etal-2026-framenet,
title = "{F}rame{N}et Semantic Role Classification by Analogy",
author = "Ngo, Van Duy and
Afantenos, Stergos and
Lorini, Emiliano and
Couceiro, Miguel",
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-1.291/",
doi = "10.63317/4orqo3vca85v",
pages = "3633--3644",
abstract = "In this paper, we adopt a relational view of analogies applied to Semantic Role Classification in FrameNet. We define analogies as formal relations over the Cartesian product of frame evoking lexical units and frame element pairs, which we use to construct a new dataset.Each element of this binary relation is labelled as a valid analogical instance if the frame elements share the same semantic role, or as invalid otherwise.This formulation allows us to transform Semantic Role Classification into binary classification and train a lightweight Artificial Neural Network (ANN) that exhibits rapid convergence with minimal parameters. Crucially, no Semantic Role information is introduced to the neural network during training. We recover semantic roles during inference by computing probability distributions over candidates of all semantic roles within a given frame through random sampling and analogical transfer. This approach allows us to surpass previous State of the Art results while maintaining computational efficiency and frugality."
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%0 Conference Proceedings
%T FrameNet Semantic Role Classification by Analogy
%A Ngo, Van Duy
%A Afantenos, Stergos
%A Lorini, Emiliano
%A Couceiro, Miguel
%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 ngo-etal-2026-framenet
%X In this paper, we adopt a relational view of analogies applied to Semantic Role Classification in FrameNet. We define analogies as formal relations over the Cartesian product of frame evoking lexical units and frame element pairs, which we use to construct a new dataset.Each element of this binary relation is labelled as a valid analogical instance if the frame elements share the same semantic role, or as invalid otherwise.This formulation allows us to transform Semantic Role Classification into binary classification and train a lightweight Artificial Neural Network (ANN) that exhibits rapid convergence with minimal parameters. Crucially, no Semantic Role information is introduced to the neural network during training. We recover semantic roles during inference by computing probability distributions over candidates of all semantic roles within a given frame through random sampling and analogical transfer. This approach allows us to surpass previous State of the Art results while maintaining computational efficiency and frugality.
%R 10.63317/4orqo3vca85v
%U https://aclanthology.org/2026.lrec-1.291/
%U https://doi.org/10.63317/4orqo3vca85v
%P 3633-3644
Markdown (Informal)
[FrameNet Semantic Role Classification by Analogy](https://aclanthology.org/2026.lrec-1.291/) (Ngo et al., LREC 2026)
ACL
- Van Duy Ngo, Stergos Afantenos, Emiliano Lorini, and Miguel Couceiro. 2026. FrameNet Semantic Role Classification by Analogy. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 3633–3644, Palma de Mallorca, Spain. ELRA Language Resource Association.