Bibliographic component extraction using support vector machines and hidden Markov models

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.