Static vs. Contextual Embeddings

The evolution from static to contextual embeddings represents a fundamental paradigm shift in how NLP systems represent meaning, addressing core limitations of earlier approaches while enabling significantly more powerful language understanding.

Static embeddings like Word2Vec and GloVe assign exactly one vector to each word regardless of context. While computationally efficient and interpretable, they cannot disambiguate different senses of polysemous words—'bank' receives the same representation whether it refers to a financial institution or a riverside. These embeddings effectively average all possible meanings of a word into a single point in vector space.

Contextual embeddings generate dynamic representations based on the specific context in which a word appears. The word 'bank' would receive different vectors in 'river bank' versus 'bank account,' capturing the distinct meanings in each usage. This context-sensitivity dramatically improves performance on tasks requiring fine-grained understanding of word meaning.

ELMo (2018) marked an important transition point, using bidirectional LSTMs to generate contextual representations. While still using static embeddings as input, it produced context-aware outputs by processing entire sentences through its recurrent architecture. This allowed different vector representations for the same word depending on usage, while maintaining computational efficiency through pre-computation.

Transformer-based embeddings from models like BERT and GPT took this approach further by leveraging self-attention to create fully contextual representations capturing complex interactions between all words in a passage. These embeddings adapt to document context, domain-specific usage patterns, and even subtle shifts in meaning based on surrounding words.

The superior performance of contextual embeddings comes at a computational cost—they require running text through deep neural networks rather than simple lookup tables. However, their ability to disambiguate meaning, handle polysemy, and capture nuanced semantic relationships has made them essential for state-of-the-art NLP systems across virtually all tasks requiring deep language understanding.