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Cross-Linguistic Situation Entity Segmentation for Discourse Analysis in Diachronic English and German Text

Paper · 2026 · Hanna Schmück, Veronika Urban, Xaver Krückl et al.

Summary

Situation Entity (SE) segmentation splits text into clause-like units built around a main verb and its referent. These units underpin discourse mode classification and event extraction. Earlier SE work on contemporary English did not explicitly say how to treat syntactically ambiguous cases. It mostly described corrections to the output of the older SPADE EDU segmenter. The authors propose explicit segmentation guidelines that cover contemporary and historical English and German. They cover hard cases such as participle clauses, which count as segments unless the participle is purely adjectival.

A small inter-annotator agreement study used Late Modern English texts from CLMEH and New High German texts from GiesKaNe, along with contemporary data. Human agreement stayed substantial across all four varieties. The authors also trained a new segmenter by fine-tuning XLM-RoBERTa-large on the SitEnt corpus. It performs well on contemporary English but degrades as the target text moves away from that domain, with historical German the hardest case. The released guidelines, data and code support diachronic and multilingual discourse analysis. The results point to in-domain training data, not the task definition, as the main obstacle.

Key points

  • Varieties covered: contemporary English, contemporary German, Late Modern English (1700–1900, from CLMEH) and New High German (1650–1900, from GiesKaNe).
  • Human agreement: average exact match falls between roughly 76.9% and 81.4% across the four varieties. This suggests the guidelines can be learned even for historical text.
  • Segmenter design: XLM-RoBERTa-large with a linear head and a CRF layer, using BI tagging and trained on SitEnt.
    • Results on the SitEnt test set: boundary F1 of about 90.5, precision 90.4, recall 90.6, exact match 74.3 and WindowDiff 7.7, averaged over 5 seeds.
    • The low WindowDiff indicates that most errors are small boundary shifts rather than missed segments.
  • Model vs. annotator exact match by variety:
    • Contemporary English: about 77–87%, close to the human level of about 81%.
    • Contemporary German: about 64–69%.
    • Late Modern English: about 64–68%.
    • New High German: about 43–49%, averaging around 45.8%, against a human level of about 75–78%.
  • Availability: code and data are public at github.com/coling-unia/sitent-segmenter-law2026.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of AugsburgAugsburg · Germany8 works · 38,342 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 3Research subjects and languages the work is about.English · 2,263 worksDiscourse analysis · 466 worksHistorical linguistics & language change · 249 works

Cite

Cite as Hanna Schmück, Veronika Urban, Xaver Krückl, Sonja Zeman, Claudia Claridge and Annemarie Friedrich. 2026. Cross-Linguistic Situation Entity Segmentation for Discourse Analysis in Diachronic English and German Text. Proceedings of the 20th Linguistic Annotation Workshop (LAW XX).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.law-main.8

@inproceedings{schmuck2026cross,
  title = {{Cross-Linguistic Situation Entity Segmentation for Discourse Analysis in Diachronic English and German Text}},
  author = {Hanna Schmück, Veronika Urban, Xaver Krückl, Sonja Zeman, Claudia Claridge and Annemarie Friedrich},
  booktitle = {Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)},
  year = {2026},
  doi = {10.18653/v1/2026.law-main.8},
  url = {https://doi.org/10.18653/v1/2026.law-main.8}
}

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