---
title: "Studying Expert-ese — Schacht 2026"
description: "The paper asks whether professional training in architecture leaves detectable marks on how people speak spontaneously."
authors: ["Carmen Schacht", "Renate Delucchi Danhier"]
year: 2026
venue: "Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026"
source: "https://aclanthology.org/2026.latechclfl-1.3/"
doi: "10.18653/v1/2026.latechclfl-1.3"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schacht-2026-studying-expert-ese-profiling-classification/"
---
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# Studying Expert-ese: Profiling and Classification of Domain-Specific Language Variation in Architecture with Traditional Machine Learning and LLMs

**Authors:** [Carmen Schacht](https://md.research.elamur.ai/free/en/authors/carmen-schacht/), Renate Delucchi Danhier · **Year:** 2026 · **Venue:** Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.latechclfl-1.3/) · [PDF](https://aclanthology.org/2026.latechclfl-1.3.pdf) · [DOI 10.18653/v1/2026.latechclfl-1.3](https://doi.org/10.18653/v1/2026.latechclfl-1.3) · [OpenAlex](https://openalex.org/W7140145003)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
The paper asks whether professional training in architecture leaves detectable marks on how people speak spontaneously. The authors treat this as "expert-ese", a domain register comparable to translationese. They use the ExpLay Corpus, which contains German oral descriptions of architectural images by trained architects and by laypeople. Profiling-UD and the DECAF framework are used to extract syntactic, morpho-syntactic and raw-text features. These features yield a linguistic profile for each group and a corpus-level divergence score. The authors then train several classifiers to separate expert from non-expert speech: two feature-based models, a small BiLSTM, and two LLMs. The groups differ only slightly overall. Even so, the BiLSTM with fastText embeddings separates them best, which suggests that semantic cues carry more signal than structural ones and that compact neural models handle small datasets well. The results support the Grammar of Space hypothesis. They also point to differences in how experts package information, beyond jargon alone, which matters for expert–layperson communication in fields such as architecture, medicine or law.

## Key points
- **Data:** ExpLay Corpus, which has matched elicitation conditions and is released under CC BY 4.0 via OSF.
- **Corpus divergence:** pairwise Jensen–Shannon divergence between the expert and lay subcorpora is 0.25. PCA and ANOVA explained little variance, and no single variable was significant.
- **Expert profile:** experts used longer tokens, longer sentences and greater lexical diversity, which is consistent with denser content-word use.
- **Classification:** logistic regression and SVM were compared with a BiLSTM and the LLMs GliClass and LLaMA 2. The fastText-based BiLSTM scored best, with F1 = 0.88.
- **Limitations:** the dataset is small, which also rules out fine-tuning. Automatic parsing may introduce errors, and only one domain was studied. Future work includes human classification tests and information-density analyses.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [History & humanities](https://md.research.elamur.ai/free/en/shelves/history/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{schacht2026studying,
  title = {{Studying Expert-ese: Profiling and Classification of Domain-Specific Language Variation in Architecture with Traditional Machine Learning and LLMs}},
  author = {Schacht, Carmen and Danhier, Renate Delucchi},
  booktitle = {Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026},
  year = {2026},
  doi = {10.18653/v1/2026.latechclfl-1.3},
  url = {https://doi.org/10.18653/v1/2026.latechclfl-1.3}
}
```

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56. Contribution score Person xpos PronType VerbForm deprel upos 0.070 0.050 0.050 0.050 0.050 0.040 The contribution of the use of person in the two sub-corpora is pointed out in Table 3. *Table 3: Contribution of the use of person in both groups.* A K Wong and M You. 1985. Entropy and distance of rand… [find](https://scholar.google.com/scholar?q=Contribution+score+Person+xpos+PronType+VerbForm+deprel+upos+0%2E070+0%2E050+0%2E050+0%2E050+0%2E050+0%2E040+The+contribution+of+the+use+of+person+in+the+two+sub%2Dcorpora+is+pointed+out+in+Table+3%2E+%2ATable+3%3A+Contr)
57. Group First Person Third Person 0.135 0.071 0.864 0.928 Experts Non-experts Menglin Xia, Ekaterina Kochmar, and Ted Briscoe. 2016. Text readability assessment for second language learners. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pages 12–22… [find](https://scholar.google.com/scholar?q=Group+First+Person+Third+Person+0%2E135+0%2E071+0%2E864+0%2E928+Experts+Non%2Dexperts+Menglin+Xia%2C+Ekaterina+Kochmar%2C+and+Ted+Briscoe%2E+2016%2E+Text+readability+assessment+for+second+language+learners%2E+In+Proceedi) [our card](https://md.research.elamur.ai/free/en/papers/zaratiana-2024-gliner-generalist-model-named-entity/)