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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.24797 |
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| _version_ | 1866915891665436672 |
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| author | Post, Claire Benét Bontempo, Paul Milliken, August Chen, Alvin Po-Chun Derby, Nicholas Khatwani, Saksham Nabieva, Sumeyye Sairam, Karthik Palmer, Alexis |
| author_facet | Post, Claire Benét Bontempo, Paul Milliken, August Chen, Alvin Po-Chun Derby, Nicholas Khatwani, Saksham Nabieva, Sumeyye Sairam, Karthik Palmer, Alexis |
| contents | To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events. Despite its importance, aspect remains sparsely annotated across semantic meaning representation frameworks. This has, in turn, hindered not only current manual annotation, but also the development of automatic systems capable of predicting aspectual information. In this paper, we introduce a new dataset of English sentences annotated with UMR aspect labels over Abstract Meaning Representation (AMR) graphs that lack the feature. We describe the annotation scheme and guidelines used to label eventive predicates according to the UMR aspect lattice, as well as the annotation pipeline used to ensure consistency and quality across annotators through a multi-step adjudication process. To demonstrate the utility of our dataset for future automation, we present baseline experiments using three modeling approaches. Our results establish initial benchmarks for automatic UMR aspect prediction and provide a foundation for integrating aspect into semantic meaning representations more broadly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24797 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Structured Meaning Representations with Aspect Classification Post, Claire Benét Bontempo, Paul Milliken, August Chen, Alvin Po-Chun Derby, Nicholas Khatwani, Saksham Nabieva, Sumeyye Sairam, Karthik Palmer, Alexis Computation and Language To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events. Despite its importance, aspect remains sparsely annotated across semantic meaning representation frameworks. This has, in turn, hindered not only current manual annotation, but also the development of automatic systems capable of predicting aspectual information. In this paper, we introduce a new dataset of English sentences annotated with UMR aspect labels over Abstract Meaning Representation (AMR) graphs that lack the feature. We describe the annotation scheme and guidelines used to label eventive predicates according to the UMR aspect lattice, as well as the annotation pipeline used to ensure consistency and quality across annotators through a multi-step adjudication process. To demonstrate the utility of our dataset for future automation, we present baseline experiments using three modeling approaches. Our results establish initial benchmarks for automatic UMR aspect prediction and provide a foundation for integrating aspect into semantic meaning representations more broadly. |
| title | Enhancing Structured Meaning Representations with Aspect Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.24797 |