How do contextual embeddings work in NLP?

Can you explain how contextual embeddings work in natural language processing?

Sure! Contextual embeddings are representations of words that take into account the context in which they appear. Unlike traditional embeddings, which assign a fixed vector to each word, contextual embeddings generate different vectors for the same word based on its surrounding words.

What are some popular models that use contextual embeddings?

Some popular models include BERT, ELMo, and GPT. These models leverage deep learning techniques to create embeddings that capture the nuances of language in context.

How do these embeddings improve NLP tasks?

They improve tasks like sentiment analysis, translation, and question answering by providing richer representations that understand word meanings based on context, leading to better performance.

Can you give an example of how context changes word meaning?

Absolutely! Take the word 'bank'. In 'river bank', it refers to the side of a river, while in 'savings bank', it refers to a financial institution. Contextual embeddings help models distinguish these meanings.