  In data dominated systems and applications, a concept of representing words
in a numerical format has gained a lot of attention. There are a few approaches
used to generate such a representation. An interesting issue that should be
considered is the ability of such representations - called embeddings - to
imitate human-based semantic similarity between words. In this study, we
perform a fuzzy-based analysis of vector representations of words, i.e., word
embeddings. We use two popular fuzzy clustering algorithms on count-based word
embeddings, known as GloVe, of different dimensionality. Words from
WordSim-353, called the gold standard, are represented as vectors and
clustered. The results indicate that fuzzy clustering algorithms are very
sensitive to high-dimensional data, and parameter tuning can dramatically
change their performance. We show that by adjusting the value of the fuzzifier
parameter, fuzzy clustering can be successfully applied to vectors of high - up
to one hundred - dimensions. Additionally, we illustrate that fuzzy clustering
allows to provide interesting results regarding membership of words to
different clusters.
