What is the difference between tokenization and embedding in NLP?

What is the difference between tokenization and embedding in NLP?

Tokenization is the process of breaking text into smaller units, like words or subwords, while embedding transforms these tokens into numerical vectors that capture their meanings.

Can you give me an example of tokenization?

Sure! For the sentence 'ChatGPT is great!', tokenization would break it down into ['ChatGPT', 'is', 'great', '!'].

And how does embedding work with those tokens?

Embedding takes those tokens and converts them into vectors. For example, 'ChatGPT' might be represented as a vector like [0.1, 0.3, 0.5] in a high-dimensional space.

Why are embeddings important in NLP?

Embeddings are crucial because they allow models to understand the semantic relationships between words, enabling better performance in tasks like translation and sentiment analysis.