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.
Constraint Satisfaction Problems are mathematical problems where a set of variables must satisfy a given set of constraints. CSPs are used in AI for…