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    <abstract>Large language models (LLMs) excel at operating at scale by leveraging social media and data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM’s ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support the latter, we introduce Chronoberg, a temporally structured corpus of English book texts spanning 250 years, curated from Project Gutenberg and enriched with a variety of temporal annotations. First, the edited nature of books enables us to quantify lexical semantic change through time-sensitive Valence-Arousal-Dominance analysis and to support historically-grounded interpretation. With the lexicons at hand, we demonstrate a need for modern LLM-based tools to better situate their contextualization of sentiment and detection of discriminatory language across various time-periods. In fact, we show how these tools struggle with diachronic shifts in meaning, emphasizing the need for temporally aware training and evaluation pipelines, and positioning Chronoberg as a scalable resource for respective studies.</abstract>
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%0 Conference Proceedings
%T Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis
%A Hegde, Niharika
%A Paul, Subarnaduti
%A Frey, Lars-Joel
%A Brack, Manuel
%A Kersting, Kristian
%A Mundt, Martin
%A Schramowski, Patrick
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hegde-etal-2026-chronoberg
%X Large language models (LLMs) excel at operating at scale by leveraging social media and data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM’s ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support the latter, we introduce Chronoberg, a temporally structured corpus of English book texts spanning 250 years, curated from Project Gutenberg and enriched with a variety of temporal annotations. First, the edited nature of books enables us to quantify lexical semantic change through time-sensitive Valence-Arousal-Dominance analysis and to support historically-grounded interpretation. With the lexicons at hand, we demonstrate a need for modern LLM-based tools to better situate their contextualization of sentiment and detection of discriminatory language across various time-periods. In fact, we show how these tools struggle with diachronic shifts in meaning, emphasizing the need for temporally aware training and evaluation pipelines, and positioning Chronoberg as a scalable resource for respective studies.
%U https://aclanthology.org/2026.emnlp-main.2515/
%P 57185-57219

Markdown (Informal)

[Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis](https://aclanthology.org/2026.emnlp-main.2515/) (Hegde et al., EMNLP 2026)

• Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis (Hegde et al., EMNLP 2026)

ACL

• Niharika Hegde, Subarnaduti Paul, Lars-Joel Frey, Manuel Brack, Kristian Kersting, Martin Mundt, and Patrick Schramowski. 2026. Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57185–57219, Budapest, Hungary. Association for Computational Linguistics.