What are static embeddings?

Static embeddings are representations of words in a continuous vector space that remain unchanged regardless of the context in which the words appear. This means that each word, once embedded, has a fixed vector representation that does not adapt based on the other words around it. Examples of static embeddings include models like Word2Vec and GloVe, where each word is assigned a single vector that represents its meaning based solely on its usage across a corpus of text.

In contrast, embeddings that vary according to context or are dynamic and context-sensitive, such as those produced by models like ELMo or BERT, adjust the word vectors based on the specific sentence or surrounding words. These embeddings highlight the nuances of word meaning that can change depending on context, while static embeddings do not capture such variability. Static embeddings do not become outdated or irrelevant; rather, they have specific use cases where fixed representations might be advantageous for tasks that do not require nuanced contextual understanding.