论文标题
使用单级方法的COVID-19学术文章的特异性挖掘
Target specific mining of COVID-19 scholarly articles using one-class approach
论文作者
论文摘要
近年来，在电晕病毒领域发表了几篇研究文章，引起了严重的急性呼吸综合症（SARS），中东呼吸综合征（MERS）和Covid-19。在存在众多研究文章的情况下，提取最适合的文章是耗时的，并且手动不切实际。本文的目的是使用机器学习方法提取相关研究文章的活动和趋势。 COVID-19开放研究数据集（CORD-19）用于实验，而基于域知识的几个目标任务以及解释以及分类的定义。聚类技术用于创建可用文章的不同群集，然后使用并行的单级支持向量机（OCSVM）执行任务分配。具有原始和降低功能的实验验证了该方法的性能。显然，K-均值聚类算法，然后是平行的OCSVM，优于原始特征空间和减少特征空间的其他方法。
In recent years, several research articles have been published in the field of corona-virus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of corona-virus related research articles using machine learning approaches. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.
