Making the H-index more relevant: A step towards standard classes for citation classification

Abstract

The H-index is gaining popularity as a way of measuring the research impact of an academic paper. However, it has been criticized because it gives all citations equal weight. Citation classification can solve this criticism by categorising citations based on the purpose or function of the citation. An important element for performing citation classification is the presence of a standard set of classes (known as a classification scheme) to enable the comparison between the accuracy of the different techniques currently used to perform citation classification. Such a standard scheme is not available and therefore we aim to fill this gap by generating a citation classification scheme automatically. The scheme is generated by clustering four large datasets of sentences containing citations using X-means. The main contribution of this research is adapting the similarity distance between verbs extracted from the citation sentences using WordNet.