Using deep learning to analyse the times of the UN Security Council

作     者：Blanke, Tobias 

作者机构：Univ Amsterdam Inst Log Language & Computat Sci Pk 107 NL-1098 XG Amsterdam Netherlands 

出 版 物：《DIGITAL SCHOLARSHIP IN THE HUMANITIES》 (Dig. Scholarsh. Humanit.)

年 卷 期：2024年第39卷第2期

页      面：485-499页

核心收录：

学科分类：0303[法学-社会学] 0502[文学-外国语言文学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：AI4Media-A European Excellence Centre for Media  Society and Democracy (EC) 

主　　题：Deep learning text regression transfer learning explainability United Nations Security Council 

摘      要：This article analyses how digital humanities scholarship can make use of recent advances in deep learning to analyse the temporal relations in an online textual archive. We use transfer learning as well as data augmentation techniques to investigate changes in United Nations Security Council resolutions. Instead of pre-defined periods, as it is common, we target the years directly. Such a text regression task is novel in the digital humanities as far as we can see and has the advantage of speaking directly to historical relations. We present not only very good experimental results but also demonstrate how such text regressions can be interpreted directly and with surrogate topic models.