Enhancing Structured Meaning Representations with Aspect Classification

2026-03-25

AI Summary

This work addresses the absence of aspectual annotation in existing structured semantic representations such as Abstract Meaning Representation (AMR), which limits the ability to capture the internal temporal structure of events—including states, activities, and completive phases—and hinders progress in both manual annotation and automatic prediction systems. To bridge this gap, we systematically integrate the Uniform Meaning Representation (UMR) aspect framework into AMR, constructing the first English AMR-UMR aspect-annotated dataset. A multi-stage human adjudication protocol ensures high inter-annotator consistency. Leveraging this resource, we implement and evaluate three automatic aspect classification approaches, establishing the first benchmark for automatic UMR aspect prediction. Our contribution fills a critical void in structured semantic representation by incorporating aspectual information, providing a high-quality foundation for future research on aspect modeling and its integration with general-purpose semantic frameworks.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• aspect
• semantic representation
• annotation
• UMR
• AMR

Innovation

• aspect classification
• Uniform Meaning Representation (UMR)
• Abstract Meaning Representation (AMR)
• semantic annotation
• eventive predicates