Embeddings play a crucial role in semantic information retrieval (IR) by transforming words, phrases, or documents into dense vector representations that capture their meaning. These vectors enable the system to understand the semantic relationships between terms, allowing for more accurate and contextually relevant search results.
For example, embeddings like Word2Vec, GloVe, or BERT map words into high-dimensional spaces where words with similar meanings are located closer together. In a search engine, embeddings allow the system to match queries and documents based not just on keyword overlap, but also on the underlying meaning of the terms.
In semantic IR, embeddings help bridge the gap between literal keyword matching and true understanding of user intent. They enable the retrieval system to find documents that are contextually similar to the query, even if the exact keywords are not present. This improves the user experience by providing more relevant results, especially for complex or ambiguous queries.