Intrinsic Subspace Evaluation of Word Embedding Representations
Yadollah Yaghoobzadeh, Hinrich Schütze · 2016
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.