Supervised labeled latent Dirichlet allocation for document categorization

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• 作者：Ximing Li (1) (2)
  Jihong Ouyang (1) (2)
  Xiaotang Zhou (1) (2)
  You Lu (1) (2)
  Yanhui Liu (1) (2)
  
  1. College of Computer Science and Technology ; Jilin University ; Changchun ; China
  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education ; Jilin University ; Changchun ; China
  
• 关键词：Supervised ; Topic modeling ; Latent Dirichlet allocation ; Multi ; label classification
• 刊名：Applied Intelligence
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：42
• 期：3
• 页码：581-593
• 全文大小：1,304 KB
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• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

Recently, supervised topic modeling approaches have received considerable attention. However, the representative labeled latent Dirichlet allocation (L-LDA) method has a tendency to over-focus on the pre-assigned labels, and does not give potentially lost labels and common semantics sufficient consideration. To overcome these problems, we propose an extension of L-LDA, namely supervised labeled latent Dirichlet allocation (SL-LDA), for document categorization. Our model makes two fundamental assumptions, i.e., Prior 1 and Prior 2, that relax the restriction of label sampling and extend the concept of topics. In this paper, we develop a Gibbs expectation-maximization algorithm to learn the SL-LDA model. Quantitative experimental results demonstrate that SL-LDA is competitive with state-of-the-art approaches on both single-label and multi-label corpora.