Group topic model: organizing topics into groups
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- 作者：Ximing Li ; Jihong Ouyang ; You Lu ; Xiaotang Zhou ; Tian Tian
- 关键词：Topic modeling ; Latent Dirichlet allocation ; Group ; Variational inference ; Online learning ; Document clustering
- 刊名：Information Retrieval
- 出版年：2015
- 出版时间：February 2015
- 年：2015
- 卷：18
- 期：1
- 页码：1-25
- 全文大小：1,158 KB
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文摘
Latent Dirichlet allocation defines hidden topics to capture latent semantics in text documents. However, it assumes that all the documents are represented by the same topics, resulting in the “forced topic-problem. To solve this problem, we developed a group latent Dirichlet allocation (GLDA). GLDA uses two kinds of topics: local topics and global topics. The highly related local topics are organized into groups to describe the local semantics, whereas the global topics are shared by all the documents to describe the background semantics. GLDA uses variational inference algorithms for both offline and online data. We evaluated the proposed model for topic modeling and document clustering. Our experimental results indicated that GLDA can achieve a competitive performance when compared with state-of-the-art approaches.