How does Naïve Bayes compare with deep learning embeddings for NLP tasks?

Naïve Bayes uses sparse count-based features, while deep learning uses dense learned embeddings capturing semantic relationships.

Naïve Bayes relies on bag-of-words representations, ignoring word order and semantics. Deep learning models like transformers learn contextual embeddings that capture syntax and semantics. NB is faster and interpretable, but deep learning dominates in complex NLP tasks due to representation learning.