Deep neural networks can be used to create representations for words, sentences, and documents, as well as for entities, relations, and many more. They provide a dense vector to represent high-dimensional, sparse data in a compact way. Such embedding models have been show to improve the results of many text mining tasks. Further, combining these representations can reveal new insights. We investigate how these models can be used for text mining and develop new models for specific text mining tasks, such as splitting of e-mail threads.