This paper proposes a method that combines multiple models and high-confidence dictionary for event nugget detection. This method introduces dictionary features into maximum entropy model and conditional random fields model respectively, then combines the results of two models. In addition, the lexical length and the length of the dependency path between the trigger and negation or speculation in event realis recognition are considered to improve the accuracy of event realis detection. Compared to the method based on maximum entropy model, the experiment results show that proposed method can get 6.43% gain of F1 in event nugget recognition and 1.69% gain of F1 in event realis recognition.