Recurrent Convolutional Neural Networks for Text Classification

Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key…

• NN
  National Natural Science Foundation of China

  Awards: 61272332, 61202329
• NK
  National Key Research and Development Program of China

  Award: 2014CB340503