What is the difference between word embeddings and contextual embeddings?

What is the difference between word embeddings and contextual embeddings?

Word embeddings represent words in a fixed vector space, while contextual embeddings consider the context in which words appear.

Can you give me examples of each?

Sure! Word2Vec is a popular word embedding model, while BERT provides contextual embeddings by analyzing surrounding words.

Why are contextual embeddings considered better?

Contextual embeddings capture nuances and meanings based on context, making them more effective for understanding language.

Are there any limitations to contextual embeddings?

Yes, they can be computationally intensive and may require more data to train effectively compared to traditional embeddings.