A Structured Learning Approach to Temporal Relation Extraction
Ning Qiang, Zhili Feng, Dan Roth · 2017
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.