We hope that researchers will adopt the resource as a robust means of answering a diverse range of questions pertinent to similarity modeling, distributional semantics, and representation learning in general.
for models to learn high-quality representations for all linguistic concepts, we believe that future work must uncover ways to explicitly or implicitly infer "deeper," more general, conceptual properties such as intentionality, polarity, subjectivity, or concreteness (Gershman and Dyer 2014).