In this paper, we propose a novel ensemble approach for event nugget detection that consists of heterogeneous encoding models to handle diversifying linguistic expressions of events in text and a dynamic ensemble method to obtain an ensemble of reliable models for each input token dynamically. From a set of comparative evaluations in subtasks, we show that our proposed method exceeds each encoding model and soft voting in F1-score. Moreover, we prove the effectiveness of our proposal by comparing our evaluation system with the results of NIST TAC KBP2016 and KBP2017 participants in F1-scores. Lastly, we consider the usefulness of our proposed method in event nugget detection through a series of discussions on applying proposed method to recent neural network models.