The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.

这篇论文利用自然语言处理技术研究了大规模的大屠杀幸存者见证集，将见证转化为结构化问题和答案，通过主题建模方法发现了关键主题并分析了不同年龄和性别组之间的差异和相似之处，进而揭示了见证集中主题的演化和幸存者经历中的潜在变异。

利用主题建模识别犹太大屠杀证言中的叙述模式和异常情况