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Using the large, annotated dataset of about 10K citation contexts annotated by human experts, extracted from the Association for Computational Linguistics repository, we present a deep learning\u2013based citation context classification architecture. Unlike all existing state-of-the-art feature-based citation classification models, our proposed convolutional neural network (CNN) with fastText-based pre-trained embedding vectors uses only the citation context as its input to outperform them in both binary- (important and non-important) and multi-class (Use, Extends, CompareOrContrast, Motivation, Background, Other) citation classification tasks. Furthermore, we propose using focal-loss and class-weight functions in the CNN model to overcome the inherited class imbalance issues in citation classification datasets. We show that using the focal-loss function with CNN adds a factor of [Formula: see text] to the cross-entropy function. Our model improves on the baseline results by achieving an encouraging 90.6 F1 score with 90.7% accuracy and a 72.3 F1 score with a 72.1% accuracy score, respectively, for binary- and multi-class citation classification tasks.<\/jats:p>","DOI":"10.1177\/0165551521991022","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T01:03:40Z","timestamp":1616634220000},"page":"79-92","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":37,"title":["A novel focal-loss and class-weight-aware convolutional neural network for the classification of in-text citations"],"prefix":"10.1177","volume":"49","author":[{"given":"Naif Radi","family":"Aljohani","sequence":"first","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia, Jeddah, Saudi 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