  Word embedding systems such as Word2Vec and GloVe are well-known in deep
learning approaches to NLP. This is largely due to their ability to capture
semantic relationships between words. In this work we investigated their
usefulness in capturing rhythmic similarity of words instead. The results show
that vectors these embeddings assign to rhyming words are more similar to each
other, compared to the other words. It is also revealed that GloVe performs
relatively better than Word2Vec in this regard. We also proposed a first of its
kind metric for quantifying rhythmic similarity of a pair of words.
