Geometric Probing of Word Vectors.

This paper studies the informativeness of linguistic properties such as part-of-speech and named entities encoded in word representations.First, we find directions that correspond to these properties using the method of Elazar et al. (2020).Then such directions are compared with the principal vectors obtained from application of PCA to word embeddings.As a result, we find that the part-of-speech information is more important for word embeddings than the named entity property.