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

arXiv: https://arxiv.org/abs/2604.19620 · HTML: https://mlsanity.com/paper/2604.19620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 77 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
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- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Probing the contents of semantic representations from text, behavior, and brain data using the psychNorms metabase](https://mlsanity.com/paper/2412.04936/) (2024)
- [Why So Down? The Role of Negative (and Positive) Pointwise Mutual Information in Distributional Semantics](https://mlsanity.com/paper/1908.06941/) (2019)
- [The "LLM World of Words" English free association norms generated by large language models](https://mlsanity.com/paper/2412.01330/) (2024)
- [Measuring individual semantic networks: A simulation study](https://mlsanity.com/paper/2410.18326/) (2024)
- [Where meaning lives: Layer-wise accessibility of psycholinguistic features in encoder and decoder language models](https://mlsanity.com/paper/2601.03798/) (2026)

## Built on by (most cited first, 1 of 1)

- [Early Language Learning via Spreading Activation and Category Exploration in Complex Networks](https://mlsanity.com/paper/2607.06258/) (2026)

## Abstract (arXiv)

Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Although large-scale norms exist for languages such as English, Dutch, Spanish, and Mandarin Chinese, no comparable resource has been available for German. To address this gap, we present free-association norms for 5,877 German cue words as part of the German version of the multilingual Small World of Words (SWOW) project. We describe the data collection procedures, participant characteristics, and our comprehensive preprocessing pipeline before introducing the resulting SWOW-DE data set. Using data from three established psycholinguistic paradigms, we show that SWOW-DE norms robustly predict performance in lexical decision tasks, relatedness judgments, and psycholinguistic word ratings. Furthermore, we demonstrate that SWOW-DE responses compare favorably with existing German resources and provide a preliminary cross-linguistic comparison revealing both shared and language-specific association patterns, highlighting promising directions for future research. Overall, SWOW-DE represents the largest collection of German free associations to date and offers a unique resource for linguistic, psychological, and cross-cultural research.