We argue that citations, as they have different reasons and functions, should not all be treated in the same way. 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–based 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 $(1-p_t{)}^{\gamma }$ 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.

我们认为，引文由于其原因和功能不同，因此不应全部以相同的方式对待。使用由人类专家注释的大约10K引用上下文的大型注释数据集（从计算语言协会存储库中提取），我们提出了一种基于深度学习的引用上下文分类架构。与所有现有的基于特征的最新引用分类模型不同，我们提出的具有基于fastText的预训练嵌入向量的卷积神经网络（CNN）仅使用引用上下文作为其输入，因此在两个二进制（（重要和不重要的内容）和多类（使用，扩展，CompareOrContrast，动机，背景，其他）引文分类任务。此外，我们建议在CNN模型中使用失焦和类权重函数来克服引文分类数据集中的继承类不平衡问题。我们表明，将CNN与散焦功能结合使用时，会增加以下因素：$（\mathrm{1个}-p_{Ť}{）}^{\gamma }$交叉熵函数。对于二类和多类引文分类任务，我们的模型分别通过获得令人鼓舞的90.6 F1分数（准确率90.7％）和72.3 F1分数（准确率72.1％）来改善基线结果。