@techreport{oai:ipsj.ixsq.nii.ac.jp:00241816,
 author = {松谷, 憲吾 and 奥出, 真理子 and Kengo, Matsutani and Mariko, Okude},
 issue = {4},
 month = {Jan},
 note = {SNS におけるトラブルは，攻撃性が直接的ではない表現が原因となることがあり，攻撃性の推定が難しい．このような文章から攻撃性を推定する方法として，大規模言語モデルの入力に返信元文章を用いて文脈を学習する方法が提案されているが，返信文章の攻撃性の推定精度において課題があることが報告されている．そこで本研究では，大規模言語モデル BERT に，返信元文章（親文章）とそれに攻撃性を付加した文脈を学習させ，返信文章（子文章）の攻撃性を推定する新たな手法を提案する．動画共有サイトに投稿されたコメントを対象に提案手法を評価した結果，従来手法に比べて攻撃性の推定の正解率が 4% 向上することを確認した．, Problems with social networking may be caused by less direct expression of aggression, making it difficult to estimate aggression. As a method for estimating aggression from such sentences, a method for learning the context using the source sentences of replies as input for a large-scale language model has been proposed; however, it has been reported that there are issues in the accuracy of the estimation of aggression in reply sentences. In this study, we propose a new method for estimating aggression in reply to sentences (child sentences) by having the large-scale language model BERT learn the reply to the source sentence (parent sentence) and the context in which aggression is added. The proposed method was evaluated based on comments posted on video-sharing websites, and it was confirmed that the accuracy of aggression estimation was improved by 4% compared with the conventional method.},
 title = {大規模言語モデルを用いた文脈考慮によるSNSの文章の攻撃性の推定},
 year = {2025}
}