Embeddings revolutionized how machines understand language by addressing limitations in previous methods like one-hot encoding, bag-of-words, N-grams, and TF-IDF.
Early embedding models like Word2Vec captured semantic relationships by training neural networks to predict words based on context or context based on words.
Models like Word2Vec and GloVe offered single vectors for words, but contextual embeddings like BERT and GPT provided dynamic word representations based on context.
Embeddings are numerical representations in a continuous vector space that capture semantic relationships, allowing machines to process language.
Dimensions in embedding vectors represent abstract concepts that emerge during training.
Embeddings enable semantic similarity, preserve context, allow mathematical operations, and work efficiently at scale.
Dense vector representations in embeddings use fewer dimensions for efficiency and richer semantic content.
Embeddings are built on distributional semantics principle and have remarkable mathematical properties for analogical reasoning.
Transfer learning with pre-trained embeddings like BERT reduces data requirements for new applications.
Methods like Mean Pooling, Max Pooling, Weighted Mean Pooling, and Last Token Pooling offer different approaches to create embeddings for various tasks.