Study on Topic Tracking System Based on SVM
Shuping Li, Jie Zhao, Zhichao Song, Shengdong Li · 2011
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. Feature selection algorithm in VSM is an important means of data pre-processing, and it can reduce vector space dimension and improve the generalization ability of the algorithm. So we develop a topic tracking system based on SVM to study how feature dimension and the value of K-neighbors affect topic tracking. Then we get the variation law that they affect topic tracking, and add up their optimal values in topic tracking. And finally we analyze topic tracking system performance according to TDT evaluation results to find the reasons for this results.