期刊名称：IEEE Transactions on Emerging Topics in Computing
印刷版ISSN：2168-6750
出版年度：2021
卷号：9
期号：1
页码：269-279
DOI：10.1109/TETC.2018.2870179
出版社：IEEE Publishing
摘要：Every research manuscript is appreciated in the form of citations. Citations are expected to carry the essence of the underlying base paper by some rhetorical means. However, this is not true in reality. Citation manipulations are equally possible which shall be identified using research semantics. This paper discusses machine learning based approaches for analyzing research citations with the aim of finding quality research citations. On analyzing the semantics of the research manuscript and the respective citations, this paper proposes various metrics for citation quality analysis including deep cite, raw expressive power, expressive power and normalized expressive power.
关键词：Citation analysis;semantic analysis;citation quality;machine learning;text mining;availability index;article metrics;deep learning;expressive power
