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Joint Reasoning for Temporal and Causal Relations by Ning, Qiang; Feng, Zhili; Wu, Hao; Roth, Dan is a scholarly article available to read on EtoBox.

What is Joint Reasoning for Temporal and Causal Relations about?

Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and causal relations are closely related and one relation even dictates the other one in many cases. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints inherently in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.

Author

Ning, Qiang; Feng, Zhili; Wu, Hao; Roth, Dan

Published

2019

Language

EN