CNNs for Sentence Classification: Transformation of Data
Explore how convolutional neural networks originally designed for vision problems can be applied to NLP for sentence classification. Understand how to transform sentences into padded, vectorized forms using one-hot encoding, embeddings, or hashing to prepare data for CNN models. This lesson helps you grasp the practical steps of adapting CNN operations to one-dimensional textual data for effective classification.
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Though CNNs have mostly been used for computer vision tasks, nothing stops them from being used in NLP applications. But as we highlighted earlier, CNNs were originally designed for visual content. Therefore, using CNNs for NLP tasks requires somewhat more effort. This is why we started out learning about CNNs with a simple computer vision problem. CNNs are an attractive choice for machine learning problems due to the low parameter count of convolution layers. One such NLP application for which CNNs have been used effectively is sentence classification.
In sentence classification, a given sentence should be classified with a class. We’ll use a question database, where each question is labeled by what the question is about. For example, the question “Who was ...