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International Conference on Big Data Analytics and Knowledge Discovery

DaWaK 2015: Big Data Analytics and Knowledge Discovery pp 28–39Cite as

Unsupervised Semantic and Syntactic Based Classification of Scientific Citations

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9263))

Abstract

In the recent years, the number of scientific publications has increased substantially. A way to measure the impact of a publication is to count the number of citations to the paper. Thus, citations are being used as a proxy for a researcher’s contribution and influence in a field. Citation classification can provide context to the citations. To perform citation classification, supervised techniques are normally used. To the best of our knowledge there are no research that performs this task in a unsupervised manner. In this paper we present two techniques to cluster citations automatically without human intervention. This paper presents two novel techniques to cluster citations according to their contents (semantic) and the citation sentence styles (syntactic). The techniques are validated using external test sets from existing supervised citation classification studies.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Auckland, Auckland, New Zealand

    Mohammad Abdullatif, Yun Sing Koh & Gillian Dobbie

Authors
  1. Mohammad Abdullatif
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  2. Yun Sing Koh
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  3. Gillian Dobbie
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Corresponding author

Correspondence to Mohammad Abdullatif .

Editor information

Editors and Affiliations

  1. University of Science and Technology, Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University, Osaka, Japan

    Takahiro Hara

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Abdullatif, M., Koh, Y.S., Dobbie, G. (2015). Unsupervised Semantic and Syntactic Based Classification of Scientific Citations. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-22729-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22728-3

  • Online ISBN: 978-3-319-22729-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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