Initializing Convolutional Filters with Semantic Features for Text Classification

2017-09-01 · EMNLP 2017 9 · Shen Li, Zhe Zhao, Tao Liu, Renfen Hu, Xiaoyong Du

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.

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Tasks

Classification General Classification Sentiment Analysis text-classification Text ClassificationTopic Classification

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