Feature Selection Using a Semantic Hierarchy for Event Recognition and Type Classification

Event recognition and event type classification are among the important areas in text mining. A state-of-the-art approach utilizing deep-level lexical semantics and syntactic dependencies suffers from a limitation of requiring too large feature space. In this paper, we propose a novel feature selection method using a semantic hierarchy of features based on WordNet relations and syntactic dependencies. Compared to the well-known feature selection methods, our proposed method reduces the feature space significantly while keeping the same level of effectiveness. For noun events, it improves effectiveness as well as efficiency. Moreover, we expect the proposed feature selection can be applied to the other types of text classification using hierarchically organized semantic resources such as WordNet.