Matrix factorization is a classical approach to discovering latent structures in large datasets, including word co-occurrence matrices. It decomposes a matrix into lower-dimensional factors to capture word associations. The article references embeddings as inputs, and matrix factorization underlies early embedding methods like LSA. Though not directly part of attention, it informs how language models evolved. It's relevant as a precursor to the learned embeddings in Transformers. Recognizing this historical context helps appreciate advances like self-attention. It highlights the shift from statistical to neural representations.