A two-stage approach for extending event detection to new types via neural networks

Abstract

We study the event detection problem in the new type extension setting. In particular, our task involves identifying the event instances of a target type that is only specified by a small set of seed instances in text. We want to exploit the large amount of training data available for the other event types to improve the performance of this task. We compare the convolutional neural network model and the feature-based method in this type extension setting to investigate their effectiveness. In addition, we propose a two-stage training algorithm for neural networks that effectively transfers knowledge from the other event types to the target type. The experimental results show that the proposed algorithm outperforms strong baselines for this task.