Citation Intent Classification
10 papers with code • 2 benchmarks • 4 datasets
Identifying the reason why an author cited another author.
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Identifying the intent of a citation in scientific papers (e. g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature.
Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.
Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse.
For the ACL-ARC dataset, we report a 53. 86% F1 score for the zero-shot setting, which improves to 63. 61% and 66. 99% for the 5-shot and 10-shot settings, respectively.