Nowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand-crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1-score, which are 17.39% and 2.33% improvements to the state-of-the-art approach.

中文翻译：

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一种用于挖掘和检测推文中因果相关事件的深度学习模型

如今，公共聚会和社交活动已成为现代城市生活不可或缺的一部分。为了无缝运行此类事件，需要实时挖掘和监控因果相关的事件，以便管理层能够做出明智的决策并采取适当的行动。在这种情况下，从推文等短文本中自动检测事件因果关系可能对事件管理有用。然而，从推文中检测事件因果关系是一项具有挑战性的任务。推文很短、没有结构，而且通常用高度非正式的语言编写，缺乏足够的上下文信息来检测因果关系。现有的方法应用了不同的技术，包括手工制作的语言规则和机器学习模型。然而，这些方法都没有解决与缺乏上下文信息相关的问题。在本文中，我们通过应用上下文词扩展技术和深度因果事件检测模型来检测推文中的事件因果关系。上下文词扩展技术由从一百万条新闻文章中提取的背景知识驱动。我们的模型实现了 79.35% 的召回率和 67.28% 的 f1-score，比最先进的方法提高了 17.39% 和 2.33%。