Embeddings 101: Unlocking Semantic Relationships in Text

Embeddings have transformed natural language processing by representing words as dense vectors that capture semantic relationships. Unlike one-hot encoding or bag-of-words, embeddings convey context and meaning. Modern models like BERT generate context-specific word vectors, enhancing tasks such as sentiment analysis. Embeddings facilitate efficient computation, analogical reasoning, and transfer learning across NLP applications. Sentence-level embeddings can be generated using techniques like CLS token, mean pooling, or weighted averaging of word vectors.