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The "Small World of Words" German Free-Association Norms

Paper · 2026 · Samuel Aeschbach, Rui Mata, Kaidi Loo et al.

Summary

German has lacked a large free-association resource, although such norms exist for English, Dutch, Spanish, Mandarin and Slovene. The authors present SWOW-DE, the German branch of the multilingual Small World of Words citizen-science project. Volunteers saw a written cue and typed the first three words it brought to mind. Data were collected online from 2012 to 2025, and the cue list grew in rounds. Later rounds drew partly on words that participants had often given as responses to earlier cues. The raw data passed through a cleaning pipeline. It removed low-quality participants and repaired German spelling problems such as umlaut transliterations, ß versus ss, and capitalization. It also mapped regional spellings onto the current German standard. The released norms cover 5,877 cues. They predict lexical decision times, relatedness judgments and psycholinguistic word ratings, and the authors benchmark them against the text-based fastText model. They also compare well with earlier, much smaller German association sets. A first cross-linguistic comparison shows some association patterns shared across languages and some specific to German. This is the largest German association resource to date and serves psychology, linguistics, and human–machine alignment studies.

Key points

  • Raw sample: 23,999 participant sessions. Four exclusion rules were applied:
    • more than 30% multi-word responses;
    • more than 20% repeated responses;
    • under 60% of responses passing spell-check;
    • over 60% "unknown"/missing responses.
  • After filtering, 22,047 participants (91.87%) and 367,454 trials remained.
  • Hunspell initially accepted 84.47% of responses. Normalizing umlauts, ß and casing by testing variant combinations raised this to 94.36%, before further correction steps.
  • There are two releases, both for non-commercial use only:
    • "SWOW-DE 2025 R55": exactly 55 participants × 3 responses for each of 5,877 cues;
    • "SWOW-DE 2025 RAW": 6,003 cues.
  • The norms were validated on lexical decision, relatedness judgments (including SimLex-style data) and word ratings, with fastText as the text-based comparison.
  • Cue embeddings were built with a PPMI-weighted cue–response matrix reduced by SVD, using the associatoR R package. They were visualized with UMAP, and Louvain clusters were found. Code is on GitHub (samuelae/SWOW-DE-2025-Code).

Interesting ideas

  • I1. Spelling repair by brute force: generate every combination of known orthographic substitutions (umlaut forms, ß/ss, casing) and keep a candidate only if a dictionary accepts it, which gives safe, auditable normalization with no learned model.
  • I2. Source-level quality gates: drop a whole contributor when simple rates cross fixed thresholds (multi-word answers, repeats, spell-check failures, skips), instead of judging individual items one by one.
  • I3. Release a balanced core next to the full raw dump, with exactly the same number of responses per item, so that statistics are comparable across items while nothing is thrown away.
  • I4. Snowball vocabulary growth: add new cues taken from frequent responses to earlier cues, so coverage expands toward the words people actually use.
  • I5. Treat human association counts as an explicit weighted graph, turned into PPMI-based relatedness, and use it as a human benchmark for text-trained models.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (tokens): The en crate could map British/American spellings, hyphenation and casing variants onto lexicon lemmas the same way, logging which substitution produced the match.
  • I2 (learn): The same per-source rates could screen witness parsers or LLM teacher tiers, for example rates of malformed trees or repeated outputs, before their output is admitted to silver data.
  • I3 (learn): Evaluation sets could take a fixed number of examples per construction or verb class, so that per-category scores and negative controls are not skewed by uneven counts.
  • I4 (world): The ontology and verb-class lexicon could grow by adding concepts that frequently appear as arguments or neighbors of entries already covered.
  • I5 (world): The English SWOW norms could become an explainable association layer in the knowledge graph, each edge backed by response counts, and also serve as a check on corpus-derived relatedness.

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, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 worksDevelopmental and Educational Psychology · 499 worksNeuroscience · 165 worksCognitive Neuroscience · 164 works

Cite

Cite as Samuel Aeschbach, Rui Mata, Kaidi Loo, Simon De Deyne and Dirk U. Wulff. 2026. The "Small World of Words" German Free-Association Norms. arXiv:2604.19620.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2604.19620 · arXiv 2604.19620

@misc{aeschbach2026small,
  title = {{The "Small World of Words" German Free-Association Norms}},
  author = {Samuel Aeschbach, Rui Mata, Kaidi Loo, Simon {De Deyne} and Dirk U. Wulff},
  year = {2026},
  doi = {10.48550/arxiv.2604.19620},
  eprint = {2604.19620},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2604.19620}
}

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