MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification

Understand

We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification.

• MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector.
• We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets.
• This model is much simpler than comparable alternative architectures and requires substantially less training time.