Hyungsuc Kang  Janghoon Yang

10.5626/JOK.2019.46.10.1088

K-Means Word Embedding Semantic relation clustering

As the usage of artificial intelligence (AI) in natural language processing has increased, the importance of word embedding has grown significantly. This paper qualitatively analyzes the representational capability of word2vec models to structure semantic relation in terms of antonymy and hyponymy based on clustering characteristics and t-SNE distribution. To this end, a K-means clustering algorithm was applied to a set of words drawn from 10 categories. Some words in antonymy are found not to be embedded properly. This is attributed to the fact that they typically have many common attributes with a very few opposite ones. It is also observed that words in hyponymy are not properly embedded at all. This can be attributed to the fact that the hyponymic relations of those words are based on the information gathered through a learning process of a knowledge system, as opposed to a natural process of language acquisition. Thus, it appears that word2vec models based on the distributional hypothesis are limited to representing certain antonymic relations and do not properly represent hyponymic relations at all.