Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the
  Evaluative Meaning of German Personal Name Compounds
Annerose Eichel Tana Deeg André Blessing Milena Belosevic Sabine Arndt-Lappe Sabine Schulte im Walde
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.