  The increase in performance in NLP due to the prevalence of distributional
models and deep learning has brought with it a reciprocal decrease in
interpretability. This has spurred a focus on what neural networks learn about
natural language with less of a focus on how. Some work has focused on the data
used to develop data-driven models, but typically this line of work aims to
highlight issues with the data, e.g. highlighting and offsetting harmful
biases. This work contributes to the relatively untrodden path of what is
required in data for models to capture meaningful representations of natural
language. This entails evaluating how well English and Spanish semantic spaces
capture a particular type of relational knowledge, namely the traits associated
with concepts (e.g. bananas-yellow), and exploring the role of co-occurrences
in this context.
