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.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant Number JP15K00426. The computation was mainly carried out using the computer facilities at Research Institute for Information Technology, Kyushu University.
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Nakatoh, T., Hirokawa, S., Minami, T. et al. Attribute-based quality classification of academic papers. Artif Life Robotics 23, 235–240 (2018). https://doi.org/10.1007/s10015-017-0412-z
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DOI: https://doi.org/10.1007/s10015-017-0412-z