What are the applications of word embedding in AI?

What are the applications of word embedding in AI?

Word embeddings are used in various applications like natural language processing, sentiment analysis, and recommendation systems.

Can you explain how word embeddings improve sentiment analysis?

Sure! Word embeddings capture semantic relationships, allowing models to understand context and sentiment more accurately.

What are some popular algorithms for generating word embeddings?

Popular algorithms include Word2Vec, GloVe, and FastText, each with unique approaches to capturing word meanings.

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

Unlike one-hot encoding, word embeddings provide dense representations, capturing relationships and reducing dimensionality.

Are there any limitations to using word embeddings?

Yes, limitations include biases in training data and challenges in handling out-of-vocabulary words.