How does word embedding work?

Can you explain how word embedding works in natural language processing?

Sure! Word embedding is a technique used to represent words in a continuous vector space, capturing their meanings.

How does it differ from traditional one-hot encoding?

Unlike one-hot encoding, which creates sparse vectors, word embeddings produce dense vectors that capture semantic relationships.

What are some popular algorithms for generating word embeddings?

Common algorithms include Word2Vec, GloVe, and FastText, each using different methods to learn word representations.

Can you give an example of how word embeddings are used in applications?

Absolutely! They're used in tasks like sentiment analysis, machine translation, and information retrieval to improve understanding.