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Building a Hierarchy of Events and Topics for Newspaper Digital Libraries

机译:为报纸数字图书馆构建事件和主题的层次结构

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In this paper we propose an incremental hierarchical clustering algorithm for on-line event detection. This algorithm is applied to a set of newspaper articles in order to discover the structure of topics and events that they describe. In the first level, articles with a high temporal-semantic similarity are clustered together into events. In the next levels of the hierarchy, these events are successively clustered so that composite events and topics can be discovered. The results obtained for the F1-measure and the Detection Cost demonstrate the validity of our algorithm for on-line event detection tasks.
机译:在本文中,我们提出了一种用于在线事件检测的增量式层次聚类算法。该算法应用于一组报纸文章,以发现它们描述的主题和事件的结构。在第一级中,具有高度时态语义相似性的文章被聚集在一起成为事件。在层次结构的下一个级别中,这些事件将被连续聚类,以便可以发现复合事件和主题。 F1量度和检测成本获得的结果证明了我们的算法对在线事件检测任务的有效性。

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