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arXiv · 1906.04943

A Structured Learning Approach to Temporal Relation Extraction

Qiang Ning·Zhili Feng·Dan Roth
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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.

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Qiang Ning, Zhili Feng, Dan Roth. 2019-06-12. A Structured Learning Approach to Temporal Relation Extraction. https://arxiv.org/abs/1906.04943

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