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.