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        <title>Meaning Variation and Data Quality in the Corpus of Founding Era American English</title>
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Joyce</namePart>
            <namePart type="family">Nabende</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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            <namePart type="given">Mohammad</namePart>
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    <abstract>Legal scholars are increasingly using corpus based methods for assessing historical meaning. Among work focused on the so-called founding era (mid to late 18th century), the majority of such studies use the Corpus of Founding Era American English (COFEA) and rely on methods such as word counting and manual coding. Here, we demonstrate what can be inferred about meaning change and variation using more advanced NLP methods, focusing on terms in the U.S. Constitution. We also carry out a data quality assessment of COFEA, pointing out issues with OCR quality and metadata, compare diachronic change to synchronic variation, and discuss limitations when using NLP methods for studying historical meaning.</abstract>
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%0 Conference Proceedings
%T Meaning Variation and Data Quality in the Corpus of Founding Era American English
%A Card, Dallas
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F card-2025-meaning
%X Legal scholars are increasingly using corpus based methods for assessing historical meaning. Among work focused on the so-called founding era (mid to late 18th century), the majority of such studies use the Corpus of Founding Era American English (COFEA) and rely on methods such as word counting and manual coding. Here, we demonstrate what can be inferred about meaning change and variation using more advanced NLP methods, focusing on terms in the U.S. Constitution. We also carry out a data quality assessment of COFEA, pointing out issues with OCR quality and metadata, compare diachronic change to synchronic variation, and discuss limitations when using NLP methods for studying historical meaning.
%R 10.18653/v1/2025.acl-short.66
%U https://aclanthology.org/2025.acl-short.66/
%U https://doi.org/10.18653/v1/2025.acl-short.66
%P 841-856

Markdown (Informal)

[Meaning Variation and Data Quality in the Corpus of Founding Era American English](https://aclanthology.org/2025.acl-short.66/) (Card, ACL 2025)

• Meaning Variation and Data Quality in the Corpus of Founding Era American English (Card, ACL 2025)

ACL

• Dallas Card. 2025. Meaning Variation and Data Quality in the Corpus of Founding Era American English. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 841–856, Vienna, Austria. Association for Computational Linguistics.