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Modelling Citation Networks for Improving Scientific Paper Classification Performance

  • Mengjie Zhang
  • Xiaoying Gao
  • Minh Duc Cao
  • Yuejin Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4099)

Abstract

This paper describes an approach to the use of citation links to improve the scientific paper classification performance. In this approach, we develop two refinement functions, a linear label refinement (LLR) and a probabilistic label refinement (PLR), to model the citation link structures of the scientific papers for refining the class labels of the documents obtained by the content-based Naive Bayes classification method. The approach with the two new refinement models is examined and compared with the content-based Naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that both refinement models can significantly improve the system performance over the content-based method for all the training set sizes and that PLR is better than LLR when the training examples are sufficient.

Keywords

Bayesian Network Class Label Citation Network Citation Link Text Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mengjie Zhang
    • 1
    • 2
  • Xiaoying Gao
    • 1
    • 2
  • Minh Duc Cao
    • 1
  • Yuejin Ma
    • 2
  1. 1.School of Mathematics, Statistics and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand
  2. 2.Artificial Intelligence Research Centre, College of Mechanical and Electrical EngineeringAgricultural University of HebeiBaodingChina

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