# Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds

arXiv: https://arxiv.org/abs/2404.04031 · HTML: https://mlsanity.com/paper/2404.04031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 5 Apr 2024.

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

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- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://mlsanity.com/paper/2103.01664/) (2021)
- [From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?](https://mlsanity.com/paper/2305.10568/) (2023)
- [Systematicity in GPT-3's Interpretation of Novel English Noun Compounds](https://mlsanity.com/paper/2210.09492/) (2022)
- [Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations](https://mlsanity.com/paper/1805.02442/) (2018)
- [Targeted Sentiment Analysis: A Data-Driven Categorization](https://mlsanity.com/paper/1905.03423/) (2019)
- [Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication](https://mlsanity.com/paper/2202.00396/) (2022)
- [Measuring the compositionality of noun-noun compounds over time](https://mlsanity.com/paper/1906.02563/) (2019)

## Abstract (arXiv)

We present a comprehensive computational study of the under-investigated phenomenon of personal name compounds (PNCs) in German such as Willkommens-Merkel ('Welcome-Merkel'). Prevalent in news, social media, and political discourse, PNCs are hypothesized to exhibit an evaluative function that is reflected in a more positive or negative perception as compared to the respective personal full name (such as Angela Merkel). We model 321 PNCs and their corresponding full names at discourse level, and show that PNCs bear an evaluative nature that can be captured through a variety of computational methods. Specifically, we assess through valence information whether a PNC is more positively or negatively evaluative than the person's name, by applying and comparing two approaches using (i) valence norms and (ii) pretrained language models (PLMs). We further enrich our data with personal, domain-specific, and extra-linguistic information and perform a range of regression analyses revealing that factors including compound and modifier valence, domain, and political party membership influence how a PNC is evaluated.