Attribute-based quality classification of academic papers
- 302 Downloads
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.
KeywordsBibliometrics Academic paper Feature selection Machine learning SVM
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.
- 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 166–170Google Scholar
- 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 360–367Google Scholar
- 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. 1753–1764Google Scholar
- 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. 216–219Google Scholar