Word clusters distinguish themselves from word embedding models by their ability to learn from little data (Bansal et al., 2014;Qu et al., 2015); for example, in cases like (Bansal et al., 2014), word clusters outperform other kinds of representations, including word embeddings.
word clusters differentiate themselves from word embeddings by requiring estimation of many fewer parameters, and by their ability to derive qualitative representations from smaller corpora (Qu et al., 2015;Bansal et al., 2014).