An Entity Event Deduplication Method Based on Connected Subgraph

Wei Ai, Jia Xu, Hongen Shao, Ze Wang, Tao Meng
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引用次数: 1

Abstract

The news data from different sources has a high degree of repetition. How to accurately get the most timely news is the primary task of text processing and analysis. Therefore, we propose an entity event deduplication method based on a connected subgraph to address the high repetition rate of multisource events. In this paper, according to the correlation of events and development factors of events, we extract relevant features and improve the accuracy of the deduplication method. The final experimental evaluation shows that the proposed method has higher accuracy than the current deduplication methods, and can significantly improve the repeat detection rate of the entity event within the effective time.
基于连通子图的实体事件重复数据删除方法
不同来源的新闻数据具有高度的重复性。如何准确地获取最及时的新闻是文本处理和分析的首要任务。因此,我们提出了一种基于连通子图的实体事件重复删除方法,以解决多源事件的高重复率问题。本文根据事件的相关性和事件的发展因素,提取相关特征,提高重复数据删除方法的准确性。最终的实验评价表明,本文方法比现有的重复数据删除方法具有更高的准确率,能够显著提高实体事件在有效时间内的重复检测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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