Design and Implementation of Topic Detection and Tracking System on Web
Hua Yan · Jisuanji gongcheng · 2008
This paper designs and implements a Topic Detection and Tracking(TDT) system to process the huge number of natural language text on Web. It classifies the text into several categories, performs clustering in each category to get the topic. The system can detect the hot topics in real-time and track some topics selected by user. The accuracy of text classification is 92%, and the accuracy of clustering is 88%. Experiment shows the feasibility of the TDT system.