After the composition operations, we use tanh as the non-linear activation function to get a hidden representation z. Max Pooling After convolution, we get where K is dynamic and depends on the number of children of h. To transform Z to a fixed length and determine the most useful semantic and structure information, we perform a max pooling operation to Z on rows.
we obtain the vector representation x h ∈ R m of the whole subtree rooted at node h. Figure 3 shows the architecture of our proposed RCNN unit.