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Improving event co-reference by context extraction and dynamic feature weighting

机译:通过上下文提取和动态特征加权改善事件共参考

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摘要

Event co-reference is the process of identifying descriptions of the same event across sentences, documents, or structured databases. Existing event co-reference work focuses on sentence similarity models or feature based similarity models requiring slot filling. This work shows the effectiveness of using a hybrid approach where the similarity of two events is determined by a combination of the similarity of the two event descriptions, in addition to the similarity of the event context features of location and time. A dynamic weighting approach is taken to combine the three similarity scores together. The described approach provides several benefits including improving event resolution and requiring less reliance on sophisticated natural language processing.
机译:事件共指是在句子,文档或结构化数据库中标识同一事件的描述的过程。现有的事件共同参考工作集中在句子相似度模型或需要空位填充的基于特征的相似度模型上。这项工作显示了使用混合方法的有效性,其中,除了事件位置和时间的事件上下文特征的相似性之外,两个事件的相似性还取决于两个事件描述的相似性,从而确定了两个事件的相似性。采用动态加权方法将三个相似度分数组合在一起。所描述的方法提供了许多好处,包括提高事件的分辨率以及减少对复杂自然语言处理的依赖。

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