Discriminative Neural Sentence Modeling by Tree-Based Convolution
arXiv:1504.01106
Abstract
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.
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Cited by in corpus (4)
- Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
- A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
- Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification
- Refining Raw Sentence Representations for Textual Entailment Recognition via Attention