What are the differences between various word embeddings?

What are the differences between various word embeddings?

Word embeddings are representations of words in a continuous vector space. The main types include Word2Vec, GloVe, and FastText. Each has unique characteristics in how they capture semantic meaning.

Can you explain how Word2Vec works?

Word2Vec uses neural networks to learn word associations from a large corpus of text. It can be trained using two models: Continuous Bag of Words (CBOW) or Skip-Gram.

What about GloVe? How is it different from Word2Vec?

GloVe, or Global Vectors for Word Representation, is based on matrix factorization techniques. It captures global statistical information of the corpus, while Word2Vec focuses on local context.

Are there any advantages to using FastText?

Yes, FastText represents words as bags of character n-grams, which helps it understand subword information. This is particularly useful for morphologically rich languages.