W (1) is pre-trained by an autoencoder (Hinton, 1989) to capture semantic compositionality.
to alleviate the data sparsity problem, EB is first trained on a very large corpus 3 (denoted by C), and then fine-tuned on the target review corpus R. for phrasal product features, a statistic-based method in (Zhu et al., 2009) is used to detect noun phrases in R. then, an Unfolding Recursive Autoencoder (Socher et al., 2011) is trained on C to obtain embedding vectors for noun phrases.