DocumentCode :
3048418
Title :
Study on key technology of topic tracking based on VSM
Author :
Li, Shengdong ; Lv, Xueqiang ; Zhou, Qiang ; Shi, Shuicai
Author_Institution :
Chinese Inf. Process. Res. Center, Beijing Inf. Sci. & Technol. Univ., Beijing, China
fYear :
2010
fDate :
20-23 June 2010
Firstpage :
2419
Lastpage :
2423
Abstract :
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective models for topics representation. On the basis of 2 information gain algorithm and chi square ιY in VSM, we have studied how feature selection algorithm and feature dimension in VSM affect topic tracking. And 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 their optimal values can make topic tracking gain very good tracking performance. In addition, we also prove in 2 the experiment that chi square ιY in VSM has better performance for topic tracking than information gain algorithm.
Keywords :
learning (artificial intelligence); pattern classification; text analysis; VSM; feature dimension; feature selection algorithm; information gain algorithm; key technology study; text classification; topic representation model; topic tracking; vector space model; Automation; Classification algorithms; Information processing; Information science; Multimedia databases; Performance gain; Prototypes; Space technology; Testing; Text categorization; KNN; TDT Evaluation; Topic Tracking; VSM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2010 IEEE International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-5701-4
Type :
conf
DOI :
10.1109/ICINFA.2010.5512284
Filename :
5512284
Link To Document :
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