Using WordNet hypernyms and Dependency Features for phrasal-level Event Recognition and Type Classification

Abstract

The goal of this research is to devise a method for recognizing and classifying TimeML events in a more effective way. TimeML is the most recent annotation scheme for processing the event and temporal expressions in natural language processing fields. In this paper, we argue and demonstrate that unit feature dependency information and deep-level WordNet hypernyms are useful for event recognition and type classification. The proposed method utilizes various features including lexical semantic and dependency-based combined features. The experimental results show that our proposed method outperforms a state-of-the-art approach, mainly due to the new strategies. Especially, the performance of noun and adjective events, which have been largely ignored and yet significant, is significantly improved.