Data Science, Machine Learning, NLP, Word Embeddings, AI.
won’t get an accurate impression of word positions if you choose a couple of axes at random. In practice, people will often use dimensionality reduction methods such as t-SNE or PCA to project the high-dimensional embedded points into a lower-dimensional space (with some loss of information).