引用
计算机科学
情报检索
卷积神经网络
度量(数据仓库)
数据科学
背景(考古学)
嵌入
图形
开放式研究
数据挖掘
人工智能
万维网
理论计算机科学
地理
考古
作者
Naif Radi Aljohani,Ayman G. Fayoumi,Saeed‐Ul Hassan
出处
期刊:Applied sciences
[Multidisciplinary Digital Publishing Institute]
日期:2021-11-19
卷期号:11 (22): 10970-10970
被引量:5
摘要
We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.
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