Investigation of the structural features of word co-occurrence networks with increasing numbers of connected words

Abstract

Word co-occurrence networks (WCNs) are a major tool used to analyze languages quantitatively. In a WCN, the vertices are words (morphemes), and the edges connect n consecutive words in a sentence on the basis of the n-gram. Most studies use WCNs transformed at n = 2. In this study, we investigated the changes in the structural features of WCNs when n increases using four types of documents for eight languages. We found that WCNs with n >= 3 reflect features of the languages that do not appear when n = 2 and that some structural features evaluated by network measures depend on the text data.