# A Structured Learning Approach to Temporal Relation Extraction Source: [A Structured Learning Approach to Temporal Relation Extraction]() Collection index: [llms.txt]() Data updated: 2026-01-01T12:44:51.901Z Authors: Qiang Ning, Zhili Feng, Dan Roth Published: 2019-06-12T05:07:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Temporal Relation Classification on TempEval-3 (Platinum)](): Temporal Relation Classification; TempEval-3 (Platinum); Precision: 69.1. - [Temporal Awareness on TimeBank-Dense (test)](): Temporal Awareness; TimeBank-Dense (test); Precision: 45.57. - [Temporal Awareness on TempEval-3 PT (test)](): Temporal Awareness; TempEval-3 PT (test); Precision: 35.5. ## Original sources and implementation [Original paper]() [PDF]()