Region-Based Ranking in Association Analysis for News Relation Discovery

N. Kittiphattanabawon, T. Theeramunkong, E. Nantajeewarawat
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Abstract

Using an association-based technique to find associations among news documents can obtain useful news relations. However, existing works may not detect meaningful relations since only single association measure was used to mine news relations. This paper presents a region-based ranking approach to selectively use different association measures for different ranking regions, towards improvement of the ranking mechanism for news relation discovery. To evaluate region-based ranking, the method is compared to the conventional ranking method, which has no region construction. As performance evaluation, the top-k results of each method are compared using rank-order mismatch (ROM). Compared to the non-region method, the region-based method can find meaningful relations among news with the average ROM improvement of 1.21% - 28.32% for confidence and 4.83% - 29.04% for conviction, respectively.
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基于区域排序的新闻关联分析
使用基于关联的技术来查找新闻文档之间的关联,可以获得有用的新闻关系。然而,现有的工作可能没有发现有意义的关系,因为只有单一的关联度量来挖掘新闻关系。本文提出了一种基于区域的排序方法,针对不同的排序区域有选择地使用不同的关联度量,以改进新闻关系发现的排序机制。为了评价基于区域的排序方法,将该方法与不构建区域的传统排序方法进行了比较。作为性能评估,使用秩序不匹配(ROM)对每种方法的top-k结果进行比较。与非区域方法相比,基于区域的方法可以发现新闻之间有意义的关系,置信度和定罪度的平均ROM分别提高了1.21% ~ 28.32%和4.83% ~ 29.04%。
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