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.
Dimensionality Reduction is the process of reducing the number of variables or features in a dataset while retaining its essential information. By eliminating irrelevant…