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Word embedding systems such as Word2Vec and GloVe are well-known in deeplearning approaches to NLP. This is largely due to their ability to capturesemantic relationships between words. In this work we investigated theirusefulness in capturing rhythmic similarity of words instead. The results showthat vectors these embeddings assign to rhyming words are more similar to eachother, compared to the other words. It is also revealed that GloVe performsrelatively better than Word2Vec in this regard. We also proposed a first of itskind metric for quantifying rhythmic similarity of a pair of words.

Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words