What is contextual embedding?

Can you explain what contextual embedding is?

Contextual embedding refers to a technique in natural language processing where words are represented in a way that captures their meanings based on the context in which they appear.

How does it differ from traditional word embeddings?

Unlike traditional word embeddings, which assign a fixed vector to each word, contextual embeddings generate different vectors for the same word depending on its surrounding words.

Can you give me an example of how it works?

Sure! In the sentence 'The bank can refuse to lend money,' the word 'bank' refers to a financial institution, while in 'I sat by the bank of the river,' it refers to the side of a river. Contextual embeddings capture these differences.

What are some popular models that use contextual embeddings?

Popular models include BERT, ELMo, and GPT, which utilize contextual embeddings to improve understanding and generation of language.