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Attribute-based quality classification of academic papers

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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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Notes

  1. Generic Engine for Transposable Association, http://geta.ex.nii.ac.jp/e/index.html.

References

  1. Garfield E (1955) Citation indexes for science: a new dimension in documentation through association of ideas. Science 122(3159):108–111

    Article  Google Scholar 

  2. Garfield E (2006) The history and meaning of the journal impact factor. J Am Med Assoc 295(1):90–93

    Article  Google Scholar 

  3. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA 102(46):16569–16572

    Article  MATH  Google Scholar 

  4. Martin BR (1996) The use of multiple indicators in the assessment of basic research. Scientometrics 36(3):343–362

    Article  Google Scholar 

  5. Nakatoh T, Nakanishi H, Baba K, Hirokawa S (2015) Focused citation count: a combined measure of relevancy and quality. In: IIAI 4th International Congress on Advanced Applied Informatics (IIAI AAI 2015), pp 166–170

  6. Sakai T, Hirokawa S (2012) Feature words that classify problem sentence in scientific article. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services (IIWAS ’12). ACM, New York, pp 360–367

  7. Ashok VG, Feng S, Choi Y (2013) Success with style: using writing style to predict the success of novels. In: 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1753–1764

  8. Otani S, Tomiura Y (2014) Extraction of key expressions indicating the important sentence from article abstracts. In: IIAI 3rd International Conference on Advanced Applied Informatics, pp. 216–219

  9. Zahedi Z, Costas R, Wouters P (2014) How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications. Scientometrics 101(2):1491–1513

    Article  Google Scholar 

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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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Authors and Affiliations

  1. Research Institute for Information Technology, Kyushu University, 744 Motooka Nishi-ku, Fukuoka, 819-0395, Japan

    Tetsuya Nakatoh, Sachio Hirokawa & Takeshi Nanri

  2. Kyushu Institute of Information Sciences, Fukuoka, Japan

    Toshiro Minami

  3. National Institute of Informatics, Tokyo, Japan

    Miho Funamori

Authors
  1. Tetsuya Nakatoh
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  2. Sachio Hirokawa
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  3. Toshiro Minami
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  4. Takeshi Nanri
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  5. Miho Funamori
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Corresponding author

Correspondence to Tetsuya Nakatoh.

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Cite this article

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

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