Attribute-based quality classification of academic papers

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

研究成果: ジャーナルへの寄稿記事

抄録

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.

元の言語英語
ページ(範囲)235-240
ページ数6
ジャーナルArtificial Life and Robotics
23
発行部数2
DOI
出版物ステータス出版済み - 6 1 2018

Fingerprint

Publications
Classifiers
Research Personnel
Databases
Research
Learning algorithms
Learning systems
Machine Learning

All Science Journal Classification (ASJC) codes

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

これを引用

Attribute-based quality classification of academic papers. / Nakatoh, Tetsuya; Hirokawa, Sachio; Minami, Toshiro; Nanri, Takeshi; Funamori, Miho.

:: Artificial Life and Robotics, 巻 23, 番号 2, 01.06.2018, p. 235-240.

研究成果: ジャーナルへの寄稿記事

Nakatoh, Tetsuya ; Hirokawa, Sachio ; Minami, Toshiro ; Nanri, Takeshi ; Funamori, Miho. / Attribute-based quality classification of academic papers. :: Artificial Life and Robotics. 2018 ; 巻 23, 番号 2. pp. 235-240.
@article{59c5cd621f4548308ea3b64e1fb9d44e,
title = "Attribute-based quality classification of academic papers",
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.",
author = "Tetsuya Nakatoh and Sachio Hirokawa and Toshiro Minami and Takeshi Nanri and Miho Funamori",
year = "2018",
month = "6",
day = "1",
doi = "10.1007/s10015-017-0412-z",
language = "English",
volume = "23",
pages = "235--240",
journal = "Artificial Life and Robotics",
issn = "1433-5298",
publisher = "Springer Japan",
number = "2",

}

TY - JOUR

T1 - Attribute-based quality classification of academic papers

AU - Nakatoh, Tetsuya

AU - Hirokawa, Sachio

AU - Minami, Toshiro

AU - Nanri, Takeshi

AU - Funamori, Miho

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85035784586&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85035784586&partnerID=8YFLogxK

U2 - 10.1007/s10015-017-0412-z

DO - 10.1007/s10015-017-0412-z

M3 - Article

AN - SCOPUS:85035784586

VL - 23

SP - 235

EP - 240

JO - Artificial Life and Robotics

JF - Artificial Life and Robotics

SN - 1433-5298

IS - 2

ER -