Joint Event Extraction via Recurrent Neural Networks

Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures. In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the…