  This paper proposes a tree-based convolutional neural network (TBCNN) for
discriminative sentence modeling. Our models leverage either constituency trees
or dependency trees of sentences. The tree-based convolution process extracts
sentences' structural features, and these features are aggregated by max
pooling. Such architecture allows short propagation paths between the output
layer and underlying feature detectors, which enables effective structural
feature learning and extraction. We evaluate our models on two tasks: sentiment
analysis and question classification. In both experiments, TBCNN outperforms
previous state-of-the-art results, including existing neural networks and
dedicated feature/rule engineering. We also make efforts to visualize the
tree-based convolution process, shedding light on how our models work.
