On the one hand, in the field of Natural Language Processing (NLP), numerical Machine Learning methods (e.g., SVM, CRF) have been intensively explored and applied. Despite the good results obtained by the numerical methods, one major drawback is that they do not provide a human readable model. A promising direction is the integration of symbolic knowledge. On the other hand, research in Data Mining has progressed significantly in the last decades, through the development of advanced algorithms and techniques to extract knowledge from data in different forms. In particular, for two decades Pattern Mining has been one of the most active field in Knowledge Discovery. Recently, a new field has emerged taking benefit of both domains: Data Mining and NLP. The objective of DMNLP is thus to provide a forum to discuss how Data Mining can be interesting for NLP tasks, providing symbolic knowledge, but also how NLP can enhance data mining approaches by providing richer and/or more complex information to mine and by integrating linguistics knowledge directly in the mining process. The workshop aims at bringing together researchers from both communities in order to stimulate discussions about the cross-fertilization of those two research fields. The idea of this workshop is to discuss future directions and new challenges emerging from the cross-fertilization of Data Mining and NLP and in the same time initiate collaborations between researchers of both communities.