@techreport{oai:ipsj.ixsq.nii.ac.jp:00212216,
 author = {金沢, 輝一 and 蔵川, 圭 and 安達, 淳 and Teruhito, Kanazawa and Kei, Kurakawa and Jun, Adachi},
 issue = {1},
 month = {Jul},
 note = {学術論文の著者同定は，学術情報サービスにおけるコンテンツへの到達性を改善するだけでなく，研究力分析を支える基礎情報の整備においても重要な役割を担っている．本稿は論文と研究者の研究トピックの類似性を推定し，これに基づいて著者を同定する手法を提案する．従来，著者の所属機関情報のない書誌情報や単著論文では著者同定の精度を安定して得ることは困難とされてきた．本稿では，5 万例以上の論文著者を提案手法で同定する評価実験を実施して，著者氏名と論文のタイトルのみを用いて和英いずれにおいても F 値 0.75～0.76 の同定精度が得られることを検証した．, Identifying authors of academic articles is an important process in developing research intelligence as well as in improving the accessibility of academic information services. In this paper, we propose a method of identifying authors based on the similarity of the subject between the article and the researches of candidate researchers. It had been considered difficult to obtain stable accuracy in identifying single authors and authors without affiliation information. However, we concluded that the proposed method using only author names and article titles achieved F-measure values of 0.75 to 0.76 in our experiment to identify more than 50,000 authors, regardless of Japanese or English the titles are written in.},
 title = {Next Sentence Predictionを応用した学術文献著者同定},
 year = {2021}
}