A comparison and analysis of models for event trigger detection

Interpreting event mentions in text is cen-tral to many tasks from scientific research to intelligence gathering. We present an event trigger detection system and explore baseline configurations. Specifically, we test whether it is better to use a single multi-class classifier or separate binary classifiers for each label. The results sug-gest that binary SVM classifiers outper-form multi-class maximum entropy by 6.4 points F-score. Brown cluster and Word-Net features are complementary with more improvement from WordNet features.