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Combining Contents and Citations for Scientific Document Classification

  • Minh Duc Cao
  • Xiaoying Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance.

Keywords

Feature Selection Information Gain Feature Selection Method Inductive Logic Programming Citation Link 
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 2005

Authors and Affiliations

  • Minh Duc Cao
    • 1
  • Xiaoying Gao
    • 1
  1. 1.School of Mathematics, Statistics & Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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