Interpretable AI

7 Understanding semantic similarity

This chapter covers

• Learning dense word representations that capture semantic meaning
• Visualizing semantic similarity of high-dimensional word embeddings using dimensionality-reduction techniques like PCA and t-SNE
• Strengths and weaknesses of PCA and t-SNE
• Validating visualizations generated by PCA and t-SNE qualitatively and quantitatively

In the previous chapter, we switched our focus from interpreting the complex processing and operations that happen within a black-box model to interpreting the representations or features learned by the model. We specifically looked at the network dissection framework to understand what concepts are learned by the feature-learning layers in a convolutional neural network ...