Text Categorization Based on Topic Model

摘要：

In the text literature,many topic models were proposed to represent documents and words as topics or latent topics in order to process text effectively and accurately.In this paper,we propose LDACLM or Latent Dirichlet Allocation Category Language Model for text categorization and estimate parameters of models by variational inference.As a variant of Latent Dirichlet Allocation Model,LDACLM regard documents of category as Language Model and use variational parameters to estimate maximum a posteriori of terms.Experiments show LDACLM model to be effective for text categorization,outperforming standard Naive Bayes and Rocchio method for text categorization.

关键词： Latent Dirichlet Allocation Variational Inference Category Language Model

作者: Shibin Zhou Kan Li Yushu Liu

作者单位: School of Computer Science and Technology Beijing Institute of Technology,Beijing 100081,P.R.China

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 572-579

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）