  The vector representation of words, known as word embeddings, has opened a
new research approach in linguistic studies. These representations can capture
different types of information about words. The grammatical gender of nouns is
a typical classification of nouns based on their formal and semantic
properties. The study of grammatical gender based on word embeddings can give
insight into discussions on how grammatical genders are determined. In this
study, we compare different sets of word embeddings according to the accuracy
of a neural classifier determining the grammatical gender of nouns. It is found
that there is an overlap in how grammatical gender is encoded in Swedish,
Danish, and Dutch embeddings. Our experimental results on the contextualized
embeddings pointed out that adding more contextual information to embeddings is
detrimental to the classifier's performance. We also observed that removing
morpho-syntactic features such as articles from the training corpora of
embeddings decreases the classification performance dramatically, indicating a
large portion of the information is encoded in the relationship between nouns
and articles.
