  Static word embeddings that represent words by a single vector cannot capture
the variability of word meaning in different linguistic and extralinguistic
contexts. Building on prior work on contextualized and dynamic word embeddings,
we introduce dynamic contextualized word embeddings that represent words as a
function of both linguistic and extralinguistic context. Based on a pretrained
language model (PLM), dynamic contextualized word embeddings model time and
social space jointly, which makes them attractive for a range of NLP tasks
involving semantic variability. We highlight potential application scenarios by
means of qualitative and quantitative analyses on four English datasets.
