Looking for the Bottleneck in Fine-grained Temporal Relation Classification

2026-04-27

AI Summary

Existing approaches to fine-grained temporal relation classification often simplify datasets by focusing only on event pairs or a subset of relations, thereby failing to model the full spectrum of interval-based temporal relationships. This work proposes a novel “point-to-interval” paradigm: it first classifies point-level relations between the endpoints of temporal intervals and then logically decodes these into complete Allen interval relations. By integrating endpoint relation classification with a semantic decoding mechanism, the method achieves, for the first time, effective modeling across the full set of temporal relations. Evaluated on the TempEval-3 benchmark, the approach attains a state-of-the-art temporal awareness score of 70.1%, establishing a new performance ceiling in the field.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advancements in natural language processing, temporal relation classification remains a considerable challenge. Early attempts framed this task using a comprehensive set of temporal relations between events and temporal expressions. However, due to the task complexity, datasets have been progressively simplified, leading recent approaches to focus on the relations between event pairs and to use only a subset of relations. In this work, we revisit the broader goal of classifying interval relations between temporal entities by considering the full set of relations that can hold between two time intervals. The proposed approach, Interval from Point, involves first classifying the point relations between the endpoints of the temporal entities and then decoding these point relations into an interval relation. Evaluation on the TempEval-3 dataset shows that this approach can yield effective results, achieving a temporal awareness score of $70.1$ percent, a new state-of-the-art on this benchmark.

Problem

• temporal relation classification
• interval relations
• temporal entities
• fine-grained
• time intervals

Innovation

• temporal relation classification
• interval relations
• point-based reasoning
• natural language processing
• TempEval-3