Word Embeddings are a technique in NLP that represent words as continuous vectors in a high-dimensional space. These vectors capture semantic and syntactic relationships between words. Word embeddings are useful for tasks such as language translation, sentiment analysis, and document clustering.
Instance-Based Learning is an AI approach where models make predictions based on similarity to previously seen examples. Instead of generalising from a predefined set…