What are word embeddings and how do they work?

What are word embeddings and how do they work?

Word embeddings are numerical representations of words in a continuous vector space, capturing semantic meanings.

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

Sure! They're used in tasks like sentiment analysis, where similar words have similar embeddings, improving accuracy.

What are some popular algorithms for generating word embeddings?

Popular algorithms include Word2Vec, GloVe, and FastText, each with unique methods for creating embeddings.

How do word embeddings differ from one-hot encoding?

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

Are there any limitations to using word embeddings?

Yes, they can struggle with out-of-vocabulary words and may not capture nuanced meanings in context.