The Evolution of Embeddings

The post discusses the evolution of embeddings in natural language processing. It explores the shift from static embeddings like Glove and Word2Vec to contextualized embeddings powered by Transformer models such as BERT, DistilBERT, and ALBERT. The latter can generate context-aware representations, addressing limitations where a word's meaning changes based on context. Examples and comparisons illustrate how these models capture word semantics and syntactics more effectively.