  Determining the intended, context-dependent meanings of noun compounds like
"shoe sale" and "fire sale" remains a challenge for NLP. Previous work has
relied on inventories of semantic relations that capture the different meanings
between compound members. Focusing on Romanian compounds, whose morphosyntax
differs from that of their English counterparts, we propose a new set of
relations and test it with human annotators and a neural net classifier.
Results show an alignment of the network's predictions and human judgments,
even where the human agreement rate is low. Agreement tracks with the frequency
of the selected relations, regardless of structural differences. However, the
most frequently selected relation was none of the sixteen labeled semantic
relations, indicating the need for a better relation inventory.
