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Citation Count Prediction Using Non-technical Terms in Abstracts

机译:引文计数在摘要中使用非技术术语预测

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Researchers are required to find previous literature which is related to their research and has a scientific impact efficiently from a large number of publications. The target problem of this paper is predicting the citation count of each scholarly paper, that is, the number of citations from other scholarly papers, as the scientific impact. The authors tried to detect the high and low of the citation count of scholarly papers using only their abstracts, especially, non-technical terms used in them. They conducted a classification of abstracts of scholarly papers with high and low citation counts, and applied the classification also to the abstracts modified by deleting technical terms from them. The results of their experiments indicate that the scientific impact of a scholarly paper can be detected from information which is written in its abstract and is not related to the trend of research topics. The classification accuracy for detecting scholarly papers with the top or bottom 1% citation counts was 0.93, and that using the abstracts without technical terms was 0.90.
机译:研究人员需要找到与他们的研究有关的先前文学,并从大量出版物有效地具有科学的影响。本文的目标问题是预测每个学术论文的引文计数,即其他学术文件的引用人数,作为科学的影响。作者试图只使用他们的摘要检测学术论文的引文数量的高低,特别是在其中使用的非技术术语。他们对高低引用计数的学术论文摘要进行了分类,并将分类应用于通过从中删除技术术语修改的摘要。他们的实验结果表明,学术纸的科学影响可以从其抽象编写的信息中检测到,与研究主题的趋势无关。用顶部或底部1%引用计数检测学术论文的分类准确性为0.93,并且使用没有技术术语的摘要为0.90。

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