What are the differences between various word embeddings?

What are the differences between various word embeddings?

Word embeddings like Word2Vec, GloVe, and FastText differ in their training methods and applications. Word2Vec uses neural networks, GloVe is based on matrix factorization, and FastText considers subword information.

Can you explain how Word2Vec works?

Word2Vec uses a shallow neural network to learn word associations from a large corpus of text. It can be trained using either the Continuous Bag of Words (CBOW) or Skip-Gram model.

What are the advantages of using GloVe over Word2Vec?

GloVe captures global statistical information of the corpus, which can lead to better representations for rare words compared to Word2Vec, which focuses more on local context.

How does FastText improve upon Word2Vec?

FastText improves Word2Vec by representing words as bags of character n-grams, allowing it to generate embeddings for out-of-vocabulary words and better handle morphological variations.