Incorporating distributed word embeddings as features has proven effective in a variety of natural language processing tasks, including parsing (Socher et al., 2013), language modeling (Bengio et al., 2003;Mnih and Hinton, 2008) and sentiment analysis (Socher et al., 2011;Labutov and Lipson, 2013;Tang et al., 2014;Tang et al., 2016).
the effectiveness of generic word embeddings has been shown to be heavily task-dependent (Labutov and Lipson, 2013;Bansal et al., 2014).