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Using WordNet Hypernyms and Dependency Features for Phrasal-Level Event Recognition and Type Classification
[chapter]
2013
Lecture Notes in Computer Science
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
doi:10.1007/978-3-642-36973-5_23
fatcat:pfprhytpx5ddbir67uw2ynekva