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Self-supervised Learning Approach for Extracting Citation Information on the Web

  • Dat T. Huynh
  • Wen Hua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7235)

Abstract

In this paper, we propose a framework for automatically training a model to extract citation information on the web. Constructing manually labeled training data to learn an extraction model is tedious, time consuming and difficult to be applied to several styles of citations with different types of entities. To eliminate the requirement of manually labeled training data, we exploit a knowledge base of citation domain and web search to derive labeled training data automatically. Our experiments show that the combination of knowledge base, heuristics and statistical methods can automate the extraction process and achieve good performance.

Keywords

Hide Markov Model Conditional Random Field Reference Table Text Segment Extraction Model 
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 2012

Authors and Affiliations

  • Dat T. Huynh
    • 1
  • Wen Hua
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandAustralia

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