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An uncertain future: Predicting events using conditional event evolutionary graph

机译:一个不确定的未来:使用条件事件进化图预测事件

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Event evolutionary graph (EEG) reflects sequential and causal relations between events, which is of great value for event prediction. However, lacking event context in the EEG raises the problems of direction uncertainty and low accuracy when making predictions. In this article, we propose a conditional event evolutionary graph (CEEG) to deal with these problems. CEEG extends EEG with an additional four types of event context, including state, cause, sub-type, and object. We first extract event context by matching the input with self-adaptive semantic templates and generalize the context for each event. To identify the evolution direction, we treat it as a binary classification problem and calculate the event transition probability for each direction given the generalized context. Experimental results show that CEEG has a strong ability to generate better event evolutionary paths compared with NAR, EEM, and other non-context-based methods.
机译:事件进化图(EEG)反映了事件之间的顺序和因果关系,这对事件预测具有很大的价值。然而,缺乏EEG中的事件上下文提出了在制定预测时提出了方向不确定性和低准确性的问题。在本文中,我们提出了一个有条件的事件进化图(CEEG)来处理这些问题。 CEEG将EEG与额外的四种类型的事件上下文扩展,包括状态,原因,子类型和对象。我们首先通过将输入与自适应语义模板匹配并概括每个事件的上下文来提取事件上下文。为了识别演化方向,我们将其视为二进制分类问题,并在给定广义上下文的每个方向上计算事件转换概率。实验结果表明,与NAR,EEM和其他基于非上下文的方法相比,CEEG具有强大的能力生成更好的事件进化路径。

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