Embeddings play a fundamental role in information retrieval (IR) by representing text data (such as queries, documents, or sentences) as continuous vectors in a high-dimensional space. These embeddings capture semantic relationships and contextual meaning, allowing the IR system to move beyond simple keyword matching.
In IR, embeddings are typically generated using models like word2vec, GloVe, or BERT, which convert words or phrases into dense vector representations. When a query is issued, the system converts the query into a vector and compares it to the embeddings of documents in the database. This enables the system to retrieve documents that are semantically similar to the query, even if they don't contain the exact keywords.
Embeddings improve search quality by handling complex queries, synonyms, and context more effectively than traditional keyword-based search methods. They are essential for tasks like semantic search, document retrieval, and recommendation systems, where capturing the meaning behind words is crucial for providing relevant results.