What are the applications of contextual embeddings in NLP?

What are the applications of contextual embeddings in NLP?

Contextual embeddings are used in various NLP tasks such as sentiment analysis, machine translation, and question answering.

Can you explain how they improve sentiment analysis?

Sure! Contextual embeddings capture the meaning of words based on their context, allowing for better understanding of sentiment nuances.

What are some popular models that use contextual embeddings?

Popular models include BERT, ELMo, and GPT, which leverage contextual embeddings for improved performance in NLP tasks.

How do these embeddings differ from traditional word embeddings?

Unlike traditional embeddings, which assign a fixed vector to each word, contextual embeddings generate dynamic vectors based on surrounding words.