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.
Feature Extraction refers to the process of identifying and selecting the most relevant features from raw data to enhance AI model performance. By extracting…