We present CoBra, a resource containing triconstituent nominal compounds in English and German. This addresses an understudied aspect of compound processing, since research and resources in psycholinguistics and NLP have mostly focused on two-constituent compounds. In addition, our resource covers both general and scientific language, allowing for a register-informed perspective on compounds. It provides syntactic and semantic annotation of compound structure, in particular of the branching direction (i.e. the internal embedding structure, the Compound Branching) and the semantic relationship between constituents. Annotations are implemented using extensions of Universal Dependencies (UD) labels. To explore applications of our new resource, we also conduct a pilot study investigating the relationship between semantic transparency and branching direction. Our results indicate that there is indeed a correlation. Overall, our resource contributes to gaining a more detailed understanding of the structure and processing of morphologically complex words within the UD framework.

We investigate the interplay of noun phrase (NP) complexity and modification type, namely the choice between pre- and postmodification, using a corpus-based approach. Our dataset is the Royal Society Corpus (RSC, Fischer et al. 2020), a diachronic corpus of English scientific writing. We find that the number of dependents, length of the head noun and distance to the head noun’s own syntactic head (typically the main verb) affect the likelihood of pre- vs. postmodification: NPs with more dependents are more likely to be premodified, NPs with a longer head noun and a head noun closer to its own head are more likely to be postmodified. In addition, we find an effect of syntactic role and definiteness as well as time: The likelihood of premodification over postmodification increases with time and subject NPs as well as indefinite NPs are more likely to be premodified than NPs in other syntactic roles or definite NPs.

We explore how different types of nominal compound complexity in scientific writing, in particular different types of compound structure, affect the reading times of experts and novices. We consider both in-domain and out-of-domain reading and use PoTeC (Jakobi et al. 2024), a corpus containing eye-tracking data of German native speakers reading passages from scientific textbooks. Our results suggest that some compound types are associated with longer reading times and that experts may not only have an advantage while reading in-domain texts, but also while reading out-of-domain.