Traditional approaches to the task of ACE event extraction are either the joint model with elaborately designed features which may lead to generalization and data-sparsity problems, or the word-embedding model based on a two-stage, multi-class classification architecture, which suffers from error propagation since event triggers and arguments are predicted in isolation. This paper proposes a novel event-extraction method that not only extracts triggers and arguments simultaneously, but also adopts a framework based on convolutional neural networks (CNNs) to extract features automatically. However, CNNs can only capture sentence-level features, so we propose the skip-window convolution neural networks (S-CNNs) to extract global structured features, which effectively capture the global dependencies of every token in the sentence. The experimental results show that our approach outperforms other state-of-the-art methods.
CITATION STYLE
Zhang, Z., Xu, W., & Chen, Q. (2016). Joint event extraction based on skip-window convolutional neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10102, pp. 324–334). Springer Verlag. https://doi.org/10.1007/978-3-319-50496-4_27
Mendeley helps you to discover research relevant for your work.