Abstract
Article citations are composed of subfields such as author, title, journal, and year. It is useful to automatically identify attributes of these subfields, since they are used for linking a citation with the actual cited article. In this article, we employ a Support Vector Machine (SVM), a method of machine learning, to automatically identify subfields. We then employ a Hidden Markov Model (HMM) to improve the identification accuracy. Information from the subfields identified by the SVM, and syntactic information analyzed by the HMM, are integrated to make an accurate identification.
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Okada, T., Takasu, A., Adachi, J. (2004). Bibliographic Component Extraction Using Support Vector Machines and Hidden Markov Models. In: Heery, R., Lyon, L. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2004. Lecture Notes in Computer Science, vol 3232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30230-8_46
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DOI: https://doi.org/10.1007/978-3-540-30230-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23013-7
Online ISBN: 978-3-540-30230-8
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