Language shifts, so NLP models need to understand those. For example, compare the use of "gay" before ~1980 and after. Some words change in spelling, like "to-m

Language shifts, so NLP models need to understand those. For example, compare the use of "gay" before ~1980 and after. Some words change in spelling, like "to-morrow" present in works around 1800 changing to "tomorrow" in current usage. Some words are also coined, like "woke" or "enshitification". If your data or models don't account for those then it can make mistakes. For example, if a model is only trained on modern sources (and does not know about Early Modern English 2nd person pronouns "thy"/"thine"/etc.) then it can easily get confused when determining parts of speech, which then affects other down-stream processing.