Topic detection based on multi-vector and secondary clustering
Yuanhua Tang · Jisuanji gongcheng yu sheji · 2012
Topic detection technology is based on news hotspot mining on Internet.To solve the traditional topic detections do not make full use of categories information and named entity in reports.So,a new topic detection method based on multi-vector similarity calculation and secondary clustering is proposed,which classifies the reports according to its site hierarchy,and uses information of characters and locations to distinguish the topics.Furthermore,it utilizes the time aggregation behavior of reports to do partial clustering on the set of reports in the same day,and then merged the results with the old topics.The experimental results show that(CDet)Norm of the new method achieves 0.197,and its performance is about 8% better than traditional methods.