Word clustering based on word2vec and semantic similarity

Domain words clustering have important theoretical and practical significance in text categorization, the ontology research, machine learning and many other research areas. The domain words clustering method in this article is a method based on word2vec and semantic similarity computation. First of all, we get the candidate word set with word2vec tools to preliminary clustering of words. Then we tectonic domain category semantic core word set and screening candidate word set by means of semantic similarity computation. Finally we get new word set belongs to the target domain and get the word set in the field of clustering. Experiments show that this method has higher recall ratio and accuracy.