Unsupervised semantic and syntactic based classification of scientific citations

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.