Word2Vec is a popular embedding algorithm that represents words as vectors in a high-dimensional space such that semantically similar words are close to each other. The algorithm has two variants: Skip-gram and Continuous Bag-of-Words (CBOW).
In the Skip-gram variant, the algorithm predicts the context words given the target word. For example, given the sentence 'The cat sat on the mat', the target word would be 'sat', and the context words would be 'The', 'cat', 'on', and 'the'. The model then adjusts the word vectors to maximize the probability of predicting the context words given the target word. The intuition behind this is that words that appear in similar contexts tend to have similar meanings.
In the CBOW variant, the algorithm predicts the target word given the context words. For example, given the same sentence as above, the context words would be 'The', 'cat', 'on', and 'the', and the target word would be 'sat'. The model then adjusts the word vectors to maximize the probability of predicting the target word given the context words.
Word2Vec is trained on a large corpus of text data and can be used to generate word embeddings for downstream natural language processing tasks such as sentiment analysis, named entity recognition, and machine translation among others. The resulting embeddings can capture semantic relationships between words such as 'king' and 'queen' or 'man' and 'woman'.
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