Word embeddings have been successfully exploited in systems for NLP tasks, such as parsing and text classification. It is intuitive that word embeddings created from a larger corpus would provide a better coverage of vocabulary. Meanwhile, word embeddings trained on a corpus related to the given task or target domain would more effectively represent the semantics of terms. However, in some emerging domains (e.g. bio-surveillance using social media data), it may be difficult to find a domain corpus that is large enough for creating effective word embeddings. To deal with this problem, we propose novel approaches that use both word embeddings created from generic and target domain corpora. Our experimental results on sentence classifi- cation tasks show that our approaches significantly improve the performance of an existing convolutional neural network that achieved state-of-the-art performances on several text classification tasks.