Attribute-based quality classification of academic papers

Tetsuya Nakatoh, Sachio Hirokawa, Toshiro Minami, Takeshi Nanri, Miho Funamori

Research output: Contribution to journalArticle

Abstract

Investigating the relevant literature is very important for research activities. However, it is difficult to select the most appropriate and important academic papers from the enormous number of papers published annually. Researchers search paper databases by combining keywords, and then select papers to read using some evaluation measure—often, citation count. However, the citation count of recently published papers tends to be very small because citation count measures accumulated importance. This paper focuses on the possibility of classifying high-quality papers superficially using attributes such as publication year, publisher, and words in the abstract. To examine this idea, we construct classifiers by applying machine-learning algorithms and evaluate these classifiers using cross-validation. The results show that our approach effectively finds high-quality papers.

Original languageEnglish
Pages (from-to)235-240
Number of pages6
JournalArtificial Life and Robotics
Volume23
Issue number2
DOIs
Publication statusPublished - Jun 1 2018

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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