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Detecting Citation Types Using Finite-State Machines

  • Minh-Hoang Le
  • Tu-Bao Ho
  • Yoshiteru Nakamori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

This paper presents a method to extract citation types from scientific articles, viewed as an intrinsic part of emerging trend detection (ETD) in scientific literature. There are two main contributions in this work: (1) Definition of six categories (types) of citations in the literature that are extractable, human-understandable, and appropriate for building the interest and utility functions in emerging trend detection models, and (2) A method to classify citation types using finite-state machines which does not require user-interactions or explicit knowledge. The experimental comparative evaluations show the high performance of the method and the proposed ETD model shows the crucial role of classified citation types in the detection of emerging trends in scientific literature.

Keywords

Hide Markov Model Concept Hierarchy Linguistic Pattern Training Sentence Citation Type 
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

  • Minh-Hoang Le
    • 1
  • Tu-Bao Ho
    • 1
  • Yoshiteru Nakamori
    • 1
  1. 1.School of Knowledge ScienceJapan Advanced Institute of Science and TechnologyIshikawaJapan

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