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