The attention mechanism determines what global information is to be used to model local context. Additionally, the attention mechanism makes the model pay attention to the important feature information of the sentence while ignoring other unimportant feature information

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Event extraction models are taken from either a traditional approach or a deep learning approach. Deep learning event extraction models can be broken down into CNN-based, RNN-based, Attention-based, Graph Convolutional Network (GCN) based, and Transformer based models.

Event extraction models

Li, Q., Li, J., Sheng, J., Cui, S., Wu, J., Hei, Y., Peng, H., Guo, S., Wang, L., Beheshti, A., & Yu, P. (2021). A Compact Survey on Event Extraction: Approaches and Applications. https://arxiv.org/abs/2107.02126.

A Compact Survey on Event Extraction: Approaches and Applications

Traditional event extraction models are typically pattern-matching methods, mainly based on syntax trees or regular expressions. A pitfall of this approach is poor portability and flexibility from domain to domain.

Traditional event extraction models

A CNN-based event extraction model captures the essential information in a sentence, and it uses a dynamic multi-pool layer to store more critical information based on event triggers and arguments

CNN-based event extraction model

In the encoding stage, it uses a RNN to summarize the context information. Furthermore, it predicts both trigger and argument roles in the prediction stage.

RNN-based event extraction model

Attention-based event extraction model

GCN-based models work to present an efficient method for directly linking words to their informative context for event detection in sentences

Graph Convolutional Network (GCN) based event extraction model

A transformer-based event extraction model promotes event extraction by using a combination of an extraction model and a generation method based on pre-trained language models

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