(2014) adapted CBOW (Mikolov et al., 2013a) to train word embeddings on different datasets: free text documents from Wikipedia, search click-through data and user query data, showing that combining them gets stronger results than using individual word embeddings in web search ranking and word similarity task.
these two papers either learned word representations on the same corpus (Turian et al., 2010) or enhanced the embedding quality by extending training corpora, not learning algorithms (Luo et al., 2014).