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CS420 Coursework: Text Classification
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Hierarchical Neural Model for Recommending Articles

CS420 Coursework: Text Classification

In this coursework, I implemented various advanced neural network architectures for a binary text classification task. Based on several successful convolutional neural networks (CNN), I proposed a novel hierarchical neural model, which is compatible with inherent hierarchical properties of documents. The experiment shows that the proposed model outperforms conventional character-level CNNs for text classification on AUC criterion. Even without sufficient training, the stacking tricks integrating existing models help further improve the final performance in the in-class kaggle competition.

Hierarchical Neural Model

In this coursework, I design a hierarchical neural model, which is shown below. For detailed information and evaluation, see my final report here.

Environment

I ran experiments on my PC.

  • Operating System: CentOS Linux release 7.3.1611 (Core)
  • CPU: Intel(R) Core(TM) i5-4460 CPU @ 3.20GHz
  • No GPU
  • Total Memory: 16142596 kB. The code is in Python 2.7 and I implemented neural networks with PyTorch.
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