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    <abstract>Descriptions are a central component of literary texts, yet their systematic identification remains a challenge. This work suggests an approach to identifying sentences describing spatial conditions in literary text. It was developed iteratively on German literary text and extended to non-literary text to evaluate its applicability across textual domains. To assess the robustness of the method, we involved both humans and a selection of state-of-the-art Large Language Models (LLMs) in annotating a collection of sentences regarding their descriptiveness and spatiality. We compare the annotations across human annotators and between humans and LLMs. The main contributions of this paper are: (1) a set of annotation guidelines for identifying spatial descriptions in literary texts, (2) a curated dataset of almost 4,700 annotated sentences of which around 500 are spatial descriptions, produced through in-depth discussion and consensus among annotators, and (3) a pilot study of automating the task of spatial description annotation of German texts. We publish the codes and all human and LLM annotations for the public to be used for research purposes only.</abstract>
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%0 Conference Proceedings
%T Annotating Spatial Descriptions in Literary and Non-Literary Text
%A Sitter, Emilie
%A Momen, Omar
%A Steig, Florian
%A Herrmann, J. Berenike
%A Zarrieß, Sina
%Y Peng, Siyao
%Y Rehbein, Ines
%S Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-262-6
%F sitter-etal-2025-annotating
%X Descriptions are a central component of literary texts, yet their systematic identification remains a challenge. This work suggests an approach to identifying sentences describing spatial conditions in literary text. It was developed iteratively on German literary text and extended to non-literary text to evaluate its applicability across textual domains. To assess the robustness of the method, we involved both humans and a selection of state-of-the-art Large Language Models (LLMs) in annotating a collection of sentences regarding their descriptiveness and spatiality. We compare the annotations across human annotators and between humans and LLMs. The main contributions of this paper are: (1) a set of annotation guidelines for identifying spatial descriptions in literary texts, (2) a curated dataset of almost 4,700 annotated sentences of which around 500 are spatial descriptions, produced through in-depth discussion and consensus among annotators, and (3) a pilot study of automating the task of spatial description annotation of German texts. We publish the codes and all human and LLM annotations for the public to be used for research purposes only.
%R 10.18653/v1/2025.law-1.26
%U https://aclanthology.org/2025.law-1.26/
%U https://doi.org/10.18653/v1/2025.law-1.26
%P 308-325

Markdown (Informal)

[Annotating Spatial Descriptions in Literary and Non-Literary Text](https://aclanthology.org/2025.law-1.26/) (Sitter et al., LAW 2025)

• Annotating Spatial Descriptions in Literary and Non-Literary Text (Sitter et al., LAW 2025)

ACL

• Emilie Sitter, Omar Momen, Florian Steig, J. Berenike Herrmann, and Sina Zarrieß. 2025. Annotating Spatial Descriptions in Literary and Non-Literary Text. In Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025), pages 308–325, Vienna, Austria. Association for Computational Linguistics.