What are the benefits of word embeddings?

What are the benefits of using word embeddings in natural language processing?

Word embeddings capture semantic meanings of words, allowing for better understanding of context and relationships.

How do word embeddings improve machine learning models?

They provide dense vector representations, reducing dimensionality and improving model performance on tasks like classification.

Can you give examples of popular word embedding techniques?

Sure! Popular techniques include Word2Vec, GloVe, and FastText, each with unique approaches to generating embeddings.

Are there any limitations to using word embeddings?

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

How do word embeddings compare to traditional one-hot encoding?

Unlike one-hot encoding, word embeddings provide a continuous vector space, capturing relationships and similarities between words.