Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification.
REF encode global semantic features into the convolutional filters instead of initializing them randomly, which helps the filters focus on learning useful n-grams.
Initializing Convolutional Filters with Semantic Features for Text Classification
EMNLP
Computer Science