Citation Intent Classification Using Transformers

As the world of scholarly research continues to grow, the intricate network of citations serves as the foundation of academic discussion, symbolizing the interweaving of concepts and the dissemination of information. The study of citations in scientific literature is important for discovering new knowledge, retrieving information, and analyzing discourse. However, manually categorizing citation functions is a slow and biased process. To address this, we conducted research on automated citation function classification in astrophysics literature by creating and evaluating deep learning models. We also introduce the FOCAL dataset, which stands for Functions of Citations in Astrophysics Literature, includes astrophysics articles with manually labelled citation functions. Our approach uses language features, citation contexts, and domain knowledge to classify citation functions. Results show that our method accurately identifies citation functions, indicating its potential for improving citation analysis.