Event detection and domain adaptation with convolutional neural networks

Abstract

We study the event detection problem us-ing convolutional neural networks (CNNs) that overcome the two fundamental limi-tations of the traditional feature-based ap-proaches to this task: complicated feature engineering for rich feature sets and er-ror propagation from the preceding stages which generate these features. The experi-mental results show that the CNNs outper-form the best reported feature-based sys-tems in the general setting as well as the domain adaptation setting without resort-ing to extensive external resources.