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    <titleInfo>
        <title>Creating a Typology of Places to Annotate Holocaust Testimonies Through Machine Learning</title>
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            <title>Proceedings of the First Workshop on Holocaust Testimonies as Language Resources (HTRes) @ LREC-COLING 2024</title>
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    <abstract>The Holocaust was not only experienced in iconic places like Auschwitz or the Warsaw ghetto. Ordinary places, such as city streets, forests, hills, and homes, were transformed by occupation and systematic violence. While most of these places are unnamed and locationally ambiguous, their omnipresence throughout post-war testimonies from witnesses and survivors of the Holocaust emphasize their undeniable importance. This paper shares a methodology for developing a typology of places in order to annotate both named and unnamed places within interview transcripts from the United States Holocaust Memorial Museum (USHMM) through a machine learning model. The approach underscores the benefits of hybrid analysis through both automated extraction and manual review to create distinct categories of places. This paper also reviews how testimony transcripts were converted into structured data for annotation and previews ongoing work to design a search engine for users to dynamically query this place-based approach to studying the Holocaust.</abstract>
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%0 Conference Proceedings
%T Creating a Typology of Places to Annotate Holocaust Testimonies Through Machine Learning
%A Liu, Christine
%A Mattingly, William J.B.
%Y Anuradha, Isuri
%Y Wynne, Martin
%Y Frontini, Francesca
%Y Plum, Alistair
%S Proceedings of the First Workshop on Holocaust Testimonies as Language Resources (HTRes) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F liu-mattingly-2024-creating
%X The Holocaust was not only experienced in iconic places like Auschwitz or the Warsaw ghetto. Ordinary places, such as city streets, forests, hills, and homes, were transformed by occupation and systematic violence. While most of these places are unnamed and locationally ambiguous, their omnipresence throughout post-war testimonies from witnesses and survivors of the Holocaust emphasize their undeniable importance. This paper shares a methodology for developing a typology of places in order to annotate both named and unnamed places within interview transcripts from the United States Holocaust Memorial Museum (USHMM) through a machine learning model. The approach underscores the benefits of hybrid analysis through both automated extraction and manual review to create distinct categories of places. This paper also reviews how testimony transcripts were converted into structured data for annotation and previews ongoing work to design a search engine for users to dynamically query this place-based approach to studying the Holocaust.
%U https://aclanthology.org/2024.htres-1.5
%P 37

Markdown (Informal)

[Creating a Typology of Places to Annotate Holocaust Testimonies Through Machine Learning](https://aclanthology.org/2024.htres-1.5) (Liu & Mattingly, htres-WS 2024)

• Creating a Typology of Places to Annotate Holocaust Testimonies Through Machine Learning (Liu & Mattingly, htres-WS 2024)

ACL

• Christine Liu and William J.B. Mattingly. 2024. Creating a Typology of Places to Annotate Holocaust Testimonies Through Machine Learning. In Proceedings of the First Workshop on Holocaust Testimonies as Language Resources (HTRes) @ LREC-COLING 2024, page 37, Torino, Italia. ELRA and ICCL.