利用事件影响关系识别文本集合中重要事件的方法

大量研究成果表明，事件在许多文本中是客观存在的，事件之间有着紧密的联系，不同的事件在文本中有不同的重要度。文中构造事件影响因子矩阵用于描述文本集合中事件之间的关联强度。在事件影响因子矩阵的基础上介绍一种利用事件影响关系识别文本集合中重要事件的方法。该方法利用事件之间特有的时间变迁关系，综合考虑事件的Hubs值和Authorities值计算事件的重要度。实验结果表明，该方法与经典的PageRank和Reverse PageRank相比，在事件排序的效果上体现更好的性能。

A Method of Identifying Important Events from Text Collection Using Event Influence Relationship

A large amount of research results show that events objectively exist in a lot of texts, having essential inherent connections between them and different event has different importance. The matrix of event influence factor is constructed to depict the associative strengths between events of text collection. Based on the matrix of event influence factor, a method of identifying important events from text collection is elaborated by using event influence relations. This method utilizes the special timed transition relations between events and synthetically considers both hubs and authorities of events to compute event importance, abbreviated to HARank (Hubs-Authorities Rank). The experimental results show that the proposed algorithm can achieve significantly better ranking results for events over the classical PageRank and Reverse PageRank algorithms.