Papers › A Structured Learning Approach to Temporal Relation Extraction
A Structured Learning Approach to Temporal Relation Extraction
Qiang Ning, Zhili Feng, Dan Roth
Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Temporal Information Extraction | TempEval-3 | Ning et al. | Temporal awareness | 67.2 | #1 of 2 | Archive leaderboard | report |
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