We derive pre-trained character-level contextual embeddings from Flair<cite> (Akbik et al., 2018)</cite> , a wordlevel embedding model, inject these into a state-ofthe-art time normalization system, and achieve major performance improvements: 51% error reduction in news and 33% in clinical notes.