Word embeddings models language by constructing dense vector representations of words that capture meaning and context, to be used in downstream tasks such as question-answering and sentiment analysis. This article explores the challenges of modelling languages, as well as the evolution of word embeddings, from Word2Vec to BERT.

Word embeddings models language by constructing dense vector representations of words that capture meaning and context, to be used in downstream tasks such as question-answering and sentiment analysis. This article explores the challenges of modelling languages, as well as the evolution of word embeddings, from Word2Vec to BERT.