Generating Politically-Relevant Event Data
Table 1: Word-based convolutional layers.
['Layer', 'Frame', 'Kernel', 'Pool']
This paper considers two neural architectures to classify a political event. The first is a 1-dimensional ConvNet with pre-trained word embedding features as described in Kim \shortcitekim2015. In short, it is a relatively shallow network with three parallel convolutional layers and two fully-connected layers. The fully-connected layers contain 150 and 4 units each. Dropout is applied to the two fully-connected layers so meaningful connections are learned by the model.