Attention Mechanisms : Information Retrieval :: Contextual Embeddings : ?

Attention mechanisms help a model pick out the most relevant pieces of data when searching a large collection, just like a search engine retrieves the best documents for a query. Contextual embeddings do the same for words: they encode each word’s meaning based on the words around it. This lets models understand nuance, disambiguate homonyms, and capture sentence‑level meaning. Consequently, contextual embeddings are essential for tasks that require deep language understanding, such as sentiment analysis or machine translation. For example, the word “bank” gets a different embedding in “river bank” versus “bank account,” enabling the model to interpret context correctly.