  We introduce a new methodology for intrinsic evaluation of word
representations. Specifically, we identify four fundamental criteria based on
the characteristics of natural language that pose difficulties to NLP systems;
and develop tests that directly show whether or not representations contain the
subspaces necessary to satisfy these criteria. Current intrinsic evaluations
are mostly based on the overall similarity or full-space similarity of words
and thus view vector representations as points. We show the limits of these
point-based intrinsic evaluations. We apply our evaluation methodology to the
comparison of a count vector model and several neural network models and
demonstrate important properties of these models.

Computation and Language