Minimally Supervised Event Causality Identification
Minimally Supervised Event Causality Identification
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发表时间：
2011-07
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通讯作者：
Q. Do;Yee Seng Chan;D. Roth
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作者：
Q. Do;Yee Seng Chan;D. Roth
本文提出了一种基于集中分布相似性方法和话语连接词的最小监督方法，用于识别上下文中事件之间的因果关系。虽然它已被证明，分布相似性可以帮助确定因果关系，我们观察到，话语联系语和特定的话语关系，他们在上下文中唤起提供额外的信息，以确定事件之间的因果关系。我们表明，在全球推理过程中结合话语关系预测和分布相似性方法，为确定事件因果关系提供了额外的改进。
This paper develops a minimally supervised approach, based on focused distributional similarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identifying causality, we observe that discourse connectives and the particular discourse relation they evoke in context provide additional information towards determining causality between events. We show that combining discourse relation predictions and distributional similarity methods in a global inference procedure provides additional improvements towards determining event causality.