GSLDA: Supervised Topic Model With Graph Regularization

摘要：

　　In this work,we study the problem of regularizing supervised topic model using graph structure.Supervised topic model generates each document independently,whereas in many applications there are links among documents,which are quite useful for refining topics.To overcome this limit of supervised topic model,we propose a regularization framework using graph structure.By leveraging both textual content and link structure,the output of the proposed model can promote effect of topic extraction and social network analysis simultaneously.Experiment results on two real datasets demonstrate the effectiveness of the proposed approach.

关键词： Supervised topic model graph regularization perplexity

作者: Qiuling Yan Dongqing Yang

作者单位: Department of Computer Science Peking University Beijing,China;College of Information Science and En Department of Computer Science Peking University Beijing,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 632-636

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）