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.
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Le, MH., Ho, TB., Nakamori, Y. (2006). Detecting Citation Types Using Finite-State Machines. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_32
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DOI: https://doi.org/10.1007/11731139_32
Publisher Name: Springer, Berlin, Heidelberg
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