Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling

arXiv:2405.02650v1 Announce Type: new
Abstract: 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 …

abstract advanced analysis apply arxiv challenges collection cs.ai cs.cl explore foundation holocaust insights language language processing modeling narrative natural natural language natural language processing nlp outliers paper patterns processing question topic modeling type usc vast