Towards Fine-grained Citation Function Classification
Xiang Li, Yifan He, Adam Meyers, Ralph Grishman · 2013
We look into the problem of recogniz-ing citation functions in scientific liter-ature, trying to reveal authors ’ rationale for citing a particular article. We intro-duce an annotation scheme to annotate ci-tation functions in scientific papers with coarse-to-fine-grained categories, where the coarse-grained annotation roughly cor-responds to citation sentiment and the fine-grained annotation reveals more about ci-tation functions. We implement a Maxi-mum Entropy-based system trained on an-notated data under this scheme to auto-matically classify citation functions in sci-entific literature. Using combined lex-ical and syntactic features, our system achieves the F-measure of 67%. 1