Challenge: Personal name compounds (PNCs) are compositions that refer to a person, such as Willkommens-Merkel ('Welcome-Meerkel') and a personal name such as Merkel.
Approach: They propose to model 321 personal name compounds and their corresponding full names at discourse level and compare two approaches to assess whether a PNC is more positively or negatively evaluative . they further enrich data with personal, domain-specific, and extra-linguistic information and perform regression analyses revealing that factors including compound and modifier valence, domain, and political party membership influence how a pnc is evaluated.
Outcome: The proposed model shows that the PNCs are perceived as more positively or negatively than their full name and that they are perceived to be more positive or negative.

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Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2025.acl-demo)

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