Word2vec is a word embedding model that learns the probability of some words being neighbours in a sentence $p_{neighbours}(w_i, w_o)$.

1. Build a dataset of adjacent words. CBOW; skipgram; negative sampling;
2. Encode the words using vectors.
3. Build a model $f(\{\theta_i\})$ to calculate the probability of the words being neighours and improve the parameters $\{\theta_i\}$ using the dataset.

Published: by ;

Lei Ma (2019). 'Word2vec', Datumorphism, 06 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/embedding/word2vec/.