Abstract
Researchers are required to find previous literature which is related to their research and has a scientific impact efficiently from a large number of publications. The target problem of this paper is predicting the citation count of each scholarly paper, that is, the number of citations from other scholarly papers, as the scientific impact. The authors tried to detect the high and low of the citation count of scholarly papers using only their abstracts, especially, non-technical terms used in them. They conducted a classification of abstracts of scholarly papers with high and low citation counts, and applied the classification also to the abstracts modified by deleting technical terms from them. The results of their experiments indicate that the scientific impact of a scholarly paper can be detected from information which is written in its abstract and is not related to the trend of research topics. The classification accuracy for detecting scholarly papers with the top or bottom 1% citation counts was 0.93, and that using the abstracts without technical terms was 0.90.
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References
Europe PMC: Europe PubMed Central. https://europepmc.org/. Accessed 5 Feb 2018
Inflection. https://pypi.org/project/inflection/. Accessed 11 May 2018
MeSH: Medical Subject Headings. https://www.nlm.nih.gov/mesh/. Accessed 5 Feb 2018
PNAS: Proceedings of the National Academy of Sciences. http://www.pnas.org/. Accessed 5 Feb 2018
Dong, Y., Johnson, R.A., Chawla, N.V.: Can scientific impact be predicted? IEEE Trans. Big Data 2(1), 18–30 (2016)
Garfield, E.: The history and meaning of the journal impact factor. JAMA 295(1), 90–93 (2006)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. PNAS 102(46), 16569–16572 (2005)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Yan, R., Tang, J., Liu, X., Shan, D. Li, X.: Citation count prediction: learning to estimate future citations for literature. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 1247–1252. ACM, New York (2011)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 15K00310.
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Baba, T., Baba, K. (2018). Citation Count Prediction Using Non-technical Terms in Abstracts. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_25
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DOI: https://doi.org/10.1007/978-3-319-95162-1_25
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