Semantic Embeddings

Semantic embeddings represent words, phrases, or even complex concepts as dense vectors in a continuous space, aiming to capture semantic meaning and relationships. Current research focuses on improving embedding quality through techniques like transformer-based encoders, hybrid search approaches combining keyword matching with embeddings, and dimension reduction methods to optimize efficiency for large-scale applications. These advancements are driving progress in diverse fields, including information retrieval, natural language processing, and multimodal data analysis, by enabling more accurate and efficient semantic search and knowledge representation.