What are the advantages of traditional embeddings in NLP?

What are the advantages of traditional embeddings in NLP?

Traditional embeddings, like Word2Vec and GloVe, capture semantic relationships and context effectively.

How do they compare to modern embeddings like BERT?

While modern embeddings like BERT capture context better, traditional embeddings are simpler and faster to train.

Are there specific use cases where traditional embeddings are preferred?

Yes, traditional embeddings are often preferred in scenarios with limited data or when computational resources are constrained.

What are some limitations of traditional embeddings?

They may struggle with polysemy and lack the contextual understanding that modern embeddings provide.