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A Model for Anticipatory Event Detection

机译:预期事件检测模型

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摘要

Event detection is a very important area of research that discovers new events reported in a stream of text documents. Previous research in event detection has largely focused on finding the first story and tracking the events of a specific topic. A topic is simply a set of related events defined by user supplied keywords with no associated semantics and little domain knowledge. We therefore introduce the Anticipatory Event Detection (AED) problem: given some user preferred event transition in a topic, detect the occurence of the transition for the stream of news covering the topic. We confine the events to come from the same application domain, in particular, mergers and acquisitions. Our experiments showed that classical cosine similarity method fails for the AED task, whereas our conceptual model-based approach, through the use of domain knowledge and named entity type assignments, seems promising. We show experimentally that an AED voting classifier operating on a vector representation with name entities replaced by types performed AED successfully.
机译:事件检测是发现文本文档流中报告的新事件的重要研究领域。以前在事件检测方面的研究主要集中在寻找第一个故事并跟踪特定主题的事件。主题只是由用户提供的关键字定义的一组相关事件,而没有关联的语义和很少的领域知识。因此,我们引入了预期事件检测(AED)问题:给定用户在主题中的某些首选事件转换,请针对涉及该主题的新闻流检测转换的发生。我们将事件限制为来自同一应用程序域,尤其是合并和收购。我们的实验表明,经典余弦相似度方法无法完成AED任务,而我们基于概念模型的方法通过使用领域知识和命名实体类型分配,似乎很有希望。我们通过实验证明,对具有矢量名称表示形式的实体进行操作的AED投票分类器可以成功执行AED。

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