New event detection based on indexing-tree and named entity
作者： Kuo Zhang , Juan Zi , Li Gang Wu
关键词: Machine learning 、 Term (time) 、 Computer science 、 Search engine indexing 、 Tree (data structure) 、 Class (biology) 、 Event (computing) 、 Task (project management) 、 Named entity 、 Artificial intelligence 、 Linguistic Data Consortium 、 Data mining
摘要: New Event Detection (NED) aims at detecting from one or multiple streams of news stories that which is reported on a new event (i.e. not previously). With the overwhelming volume available today, there an increasing need for NED system able to detect events more efficiently and accurately. In this paper we propose model speed up task by using indexing-tree dynamically. Moreover, based observation terms different types have effects task, two term reweighting approaches are proposed improve accuracy. first approach, adjust weights dynamically previous story clusters in second employ statistics training data learn named entity each class stories. Experimental results Linguistic Data Consortium (LDC) datasets TDT2 TDT3 show can both efficiency accuracy significantly, compared baseline other existing systems.
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