Unsupervised Verb Inference from Nouns Crossing Root Boundary

Inference about whether a word in one text has similar meaning to another word in the other text is an essential task in order to understand whether two texts have similar meaning. However, this inference becomes difficult especially when two words do not share a lexical root, do not have the same argument structure, or do not have the same part-of-speech. This paper presents an unsupervised approach for inferring verbs from nouns along with a new online resource PreDic (PREdicate DICtionary) that contains verbs inferred from nouns sharing similar concepts but not the root. The verbs in PreDic are categorized into three groups, enabling applications to target precision-oriented, recall-oriented, or harmony-oriented results as needed. The experiment results show that the proposed unsupervised approach performs similar to or better than WordNet and NOMLEX. Furthermore, a new domain-verb association measure is presented to show the association relationships between inferred verbs and domains to which the verbs are possibly applied.