排名作者引文网络与自动关键字提取使用词嵌入

S. Muppidi
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引用次数: 0

摘要

本文的主要目的是提出一种利用引文网络数据对研究人员和学术作者进行评估和排名的有效方法。在引文网络中对作者进行排名,预测研究领域的杰出作者,方便作者识别其研究工作领域的主要作者。流行的独立指标,如h指数、引用次数等,在对作者进行排名时并不十分可靠。作者提出了一种新的方法,称为引文增强作者排名(CeRA)。提出的方法CeRA方法利用摘要之间与内容相关的相似性,通过使用word2vec模型提取关键字。采用k -均值聚类算法提取基于域的作者聚类。最后,使用作者所在节点在集群中的页面排名来检索特定作者的排名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ranking Authors in Citation Networks with Automated Keyword Extraction using Word Embeddings
The main objective of this present article is to propose an efficient way to evaluate and rank the researchers and academic authors by using citation network dataset. To rank the authors in the citation network to predict the prominent authors in the research domain for making ease for authors for identifying the predominant authors in their area of research work. Popular independent metrics like h-index, number of citations, and so on is not very reliable when it comes to ranking authors. Author come up with a new method called Citation enhanced Ranking of Authors (CeRA) to rank authors. The proposed approach CeRA approach utilizes content-related similarity between abstracts that extract the keywords by using the word2vec model. K-means clustering algorithm is applied to extract authors clusters based on domains. Finally, specific authors rank is retrieved using the page rank of the author's node in a cluster.
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