Semantic Transparency Measures for German Compounds
This dataset contains semantic transparency measures for a set of 1,810 German compound words, such as Flughafen (airport) or Zebrastreifen (crosswalk), and is validated against human behavioral data in multiple empirical studies, as reported in the following article:
Günther, F., Marelli, M., & Bölte, J. (2020). Semantic transparency effects in German compounds: A large dataset and multiple-task investigation. Behavior Research Methods, 52, 1208-1224.
We further provide the same semantic transparency measures for a dataset of 40,475 additional German compounds, as well as for 2,061 novel German compounds.
The measures are obtained from a German distributional semantic space, where each word is represented as a high-dimensional numerical vector. Using this semantic space, modifier relatedness is computed as the cosine similarity between modifier and compound, cos(Flug, Flughafen) , and analogously head relatedness as the cosine similarity between head and compound, cos(Hafen, Flughafen). Furthermore, constituent similarity is defined as the cosine similarity between the two constituents, cos(Flug, Hafen).
Furthermore, a compositional model was employed to obtain compositional meaning representations for the compounds (i.e., the meaning that would be predicted given the combination of their constituents). With this compositional meaning, modifier composition is computed as the cosine similarity between modifier and compositional compound, cos(Flug, [Flug+Hafen]), head composition as the cosine similarity between headand compositional compound, cos(Hafen, [Flug+Hafen]), and compound compositionality as the cosine similarity between actual and compositional compound, cos(Flughafen, [Flug+Hafen]).
For more details, see the article (Günther, Marelli, & Bölte, 2020)