DocumentCode :
3434156
Title :
Discriminative LDA
Author :
Xu, Weiran ; Dong, Mingzhi ; Lin, YunHang ; Guo, Jun ; Chen, Guang
Author_Institution :
Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2010
fDate :
24-26 Sept. 2010
Firstpage :
287
Lastpage :
292
Abstract :
This paper is aim to improve the discrimination capability of LDA model through unsupervised feature selection. Experimental results show that if the interference of general word and general topic can be removed, the discrimination capability of LDA model will be increased. The key problem is how to find supervised information to evaluate features. The LDA topics are assumed reasonable. Therefore, topics will offer surprised information for word features´ selection. Constraint coming from the surprised information is added to the LDA objective function. Finally, a heuristic algorithm is presented to obtain the solution. Experiments show that the Discriminative LDA can significantly improve the information gain of topics.
Keywords :
natural language processing; text analysis; LDA model; discrimination capability; heuristic algorithm; information gain; unsupervised feature selection; Analytical models; Approximation algorithms; Data models; Heuristic algorithms; Hidden Markov models; Interference; Semantics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network Infrastructure and Digital Content, 2010 2nd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6851-5
Type :
conf
DOI :
10.1109/ICNIDC.2010.5657790
Filename :
5657790
Link To Document :
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