@inproceedings{navigli-etal-2024-nounatlas,
title = "{N}oun{A}tlas: Filling the Gap in Nominal Semantic Role Labeling",
author = "Navigli, Roberto and
Lo Pinto, Marco and
Silvestri, Pasquale and
Rotondi, Dennis and
Ciciliano, Simone and
Scir{\`e}, Alessandro",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.857",
doi = "10.18653/v1/2024.acl-long.857",
pages = "16245--16258",
abstract = "Despite significant advances in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus needing proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although automatically created, NounAtlas {--} our frame inventory {--} is subsequently fully validated. We then put forward a technique to generate silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices which enable seamless integration of our predicate inventory with its verbal counterpart, we can mix verbal and nominal data and perform robust SRL on both types of predicates.",
}
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<abstract>Despite significant advances in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus needing proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although automatically created, NounAtlas – our frame inventory – is subsequently fully validated. We then put forward a technique to generate silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices which enable seamless integration of our predicate inventory with its verbal counterpart, we can mix verbal and nominal data and perform robust SRL on both types of predicates.</abstract>
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%0 Conference Proceedings
%T NounAtlas: Filling the Gap in Nominal Semantic Role Labeling
%A Navigli, Roberto
%A Lo Pinto, Marco
%A Silvestri, Pasquale
%A Rotondi, Dennis
%A Ciciliano, Simone
%A Scirè, Alessandro
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F navigli-etal-2024-nounatlas
%X Despite significant advances in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus needing proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although automatically created, NounAtlas – our frame inventory – is subsequently fully validated. We then put forward a technique to generate silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices which enable seamless integration of our predicate inventory with its verbal counterpart, we can mix verbal and nominal data and perform robust SRL on both types of predicates.
%R 10.18653/v1/2024.acl-long.857
%U https://aclanthology.org/2024.acl-long.857
%U https://doi.org/10.18653/v1/2024.acl-long.857
%P 16245-16258
Markdown (Informal)
[NounAtlas: Filling the Gap in Nominal Semantic Role Labeling](https://aclanthology.org/2024.acl-long.857) (Navigli et al., ACL 2024)
ACL
- Roberto Navigli, Marco Lo Pinto, Pasquale Silvestri, Dennis Rotondi, Simone Ciciliano, and Alessandro Scirè. 2024. NounAtlas: Filling the Gap in Nominal Semantic Role Labeling. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16245–16258, Bangkok, Thailand. Association for Computational Linguistics.