Exploiting Contextual Word Embedding of Authorship and Title of Articles for Discovering Citation Intent Classification. (5th April 2021)

• Record Type: Journal Article
• Authors: Roman, Muhammad; Shahid, Abdul; Uddin, Muhammad Irfan; Hua, Qiaozhi; Maqsood, Shazia
• Other Names: Aziz Furqan Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-04-05
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/5554874
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The number of scientific publications is growing exponentially. Research articles cite other work for various reasons and, therefore, have been studied extensively to associate documents. It is argued that not all references carry the same level of importance. It is essential to understand the reason for citation, called citation intent or function. Text information can contribute well if new natural language processing techniques are applied to capture the context of text data. In this paper, we have used contextualized word embedding to find the numerical representation of text features. We further investigated the performance of various machine-learning techniques on the numerical representation of text. The performance of each of the classifiers was evaluated on two state-of-the-art datasets containing the text features. In the case of the unbalanced dataset, we observed that the linear Support Vector Machine (SVM) achieved 86% accuracy for the "background" class, where the training was extensive. For the rest of the classes, including "motivation, " "extension, " and "future, " the machine was trained on less than 100 records; therefore, the accuracy was only 57 to 64%. In the case of a balanced dataset, each of the classes has the same accuracy as trained on the same size of training data. Overall, SVM performed best on both of the datasets, followed by the stochastic gradient descent classifier; therefore, SVM can produce good results as text classification on top of contextual word embedding.