Data Science, Machine Learning, NLP, Word Embeddings, AI.
This is because, during training, the model learns to treat the input “cat” a certain way, by sending it through layers of transformations defined by weights and bias parameters. When the network finally sees “liger”, if its embedding is similar to “cat”, then it will take a similar path to “cat”