Classification of citations into categories such as use, refutation, comparison etc. may have several relevant applications for digital libraries such as paper browsing aids, reading recommendations, qualified citation indexing, or fine-grained impact factor calculation. Most citation classification approaches described so far heavily rely on rule systems and patterns tailored to specific science domains. We focus on a less manual approach by learning domaininsensitive features from textual, physical, and syntactic aspects. Our experiments show the effectiveness of this feature set with various machine learning algorithms on datasets of different sizes. Furthermore, we build an ensemble-style selftraining classification model and get better classification performance using only few training data, which largely reduces the manual annotation work in this task.
REF focused on a less manual approach by learning domain-insensitive features from textual, physical, and syntactic aspects To address concerns about h-index, different alternative measures are proposed (Waltman and van Eck, 2012) .
Ensemble-style Self-training on Citation Classification
IJCNLP
Computer Science