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 phrases, while embedding transforms these tokens into numerical vectors.

Can you explain why embeddings are important?

Embeddings capture semantic meaning and relationships between words, allowing models to understand context better than raw tokens.

How do tokenization and embedding work together in NLP?

Tokenization prepares the text for processing, and embeddings convert those tokens into a format that machine learning models can use.

What are some common techniques for embedding?

Common techniques include Word2Vec, GloVe, and more recently, transformer-based embeddings like BERT.