Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words

Autor:	Rezaei, Hosein
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.04833 Zobrazit plný text záznamu View this record from Arxiv