Before revision: Embeddings -- mappings from high-dimensional discrete input to lower-dimensional continuous vector spaces -- have been widely adopted in machine learning , linguistics, and computational biology as they often surface interesting and unexpected domain semantics.
After revision: Embeddings -- mappings from high-dimensional discrete input to lower-dimensional continuous vector spaces -- have been widely adopted in machine learning applications as a way to capture domain semantics.