论文标题
单词嵌入能够捕获单词的节奏相似性
Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words
论文作者
论文摘要
Word2Vec和Glove等单词嵌入系统在NLP的深度学习方法中众所周知。这在很大程度上是由于它们能够捕获单词之间的语义关系的能力。在这项工作中，我们调查了它们在捕获单词的节奏相似性方面的有用性。结果表明，与其他单词相比，这些嵌入为押韵单词分配给押韵单词的载体更相似。还揭示了手套在这方面的表现比Word2Vec相对较好。我们还提出了一个同类指标，用于量化一对单词的节奏相似性。
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.
