Study on Key Technology for Topic Tracking

摘要：

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. On the basis of Knearest neighbor (KNN) algorithm for text classification and support vector machines (SVM) algorithm for text classification, we have studied how they affect topic tracking. Then we get the variation law that they affect topic tracking, and add up their optimal values in topic tracking. Finally, TDT evaluation method proves that optimal topic tracking performance based on SVM increases by 35.134％ more than KNN.

关键词： knn svm tdt evaluation topic tracking

作者: Shengdong Li Xueqiang Lv Hongwei Wang Shuicai Shi

作者单位: Chinese Information Processing Research Center, Beijing Information Science and Technology Universit Chinese Information Processing Research Center, Beijing Information Science and Technology Universit

会议类型: 国际会议

会议名称: Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

会议地点: 宁波

会议语种:英文

页码: 275-280

在线出版日期: 2010-11-01（万方平台首次上网日期，不代表论文的发表时间）