Global optimization to recognize causal relations between events

doi:10.16511/j.cnki.qhdxxb.2017.25.043

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Abstract Classifier-based models are widely used to identify causal relations between events. However, these models only consider the relationship between two specified events while ignoring related events. Thus, the results may have many logical contradictions. This paper presents a global optimization approach to recognize causal relations between events using an inference method based on integer linear programming (ILP). This approach introduces various kinds of constraints, i.e., a basic logical relationship, causal signal words, event types and argument information constraints to improve the performance. Tests on an annotated corpus show that this global optimization approach improves the F1 score by 5.54% compared with a classifier-based model.

Keywords event relation causal relation integer linear programming (ILP) global optimization

ZTFLH:

TP391.2

Issue Date: 15 October 2017

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	LI Peifeng
	HUANG Yilong
	ZHU Qiaoming

Cite this article:

LI Peifeng,HUANG Yilong,ZHU Qiaoming. Global optimization to recognize causal relations between events[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1042-1047.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.25.043 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I10/1042

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