Labelset topic model for multi-label document classification

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• 作者：Ximing Li ; Jihong Ouyang ; Xiaotang Zhou
• 关键词：Multi ; label classification ; Topic model ; Labelset ; Label dependency
• 刊名：Journal of Intelligent Information Systems
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：46
• 期：1
• 页码：83-97
• 全文大小：483 KB
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• 作者单位：Ximing Li (1) (2)
  Jihong Ouyang (1) (2)
  Xiaotang Zhou (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
  
• 刊物类别：Computer Science
• 刊物主题：Data Structures, Cryptology and Information Theory
  Artificial Intelligence and Robotics
  Document Preparation and Text Processing
  Business Information Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7675

文摘

It has recently been suggested that assuming independence between labels is not suitable for real-world multi-label classification. To account for label dependencies, this paper proposes a supervised topic modeling algorithm, namely labelset topic model (LsTM). Our algorithm uses two labelset layers to capture label dependencies. LsTM offers two major advantages over existing supervised topic modeling algorithms: it is straightforward to interpret and it allows words to be assigned to combinations of labels, rather than a single label. We have performed extensive experiments on several well-known multi-label datasets. Experimental results indicate that the proposed model achieves performance on par with and often exceeding that of state-of-the-art methods both qualitatively and quantitatively. Keywords Multi-label classification Topic model Labelset Label dependency