Here is an excerpt from the notes of the first lecture of the course CS224n: Natural Language Processing with Deep Learning.
3 SVD Based Methods
For this class of methods to find word embeddings (otherwise known as word vectors), we first loop over a massive data set and accumulate word co-occurrence counts in some form of a matrix $X$ and then perform Singular Value Decomposition on $X$ to get a $USV^T$ decomposition. We then use the rows of $U$ as the word embeddings for all words in our dictionary. Let us discuss a few choices of $X$.
What does $USV^T$ refer to in this context?