Co-clustering, an extension of one-sided clustering, refers to process of clustering data points and features simultaneously. During text clustering tasks, traditional one-sided clustering algorithms have encountered difficulties dealing with sparse problem. Instead, a co-clustering procedure, where data's common organizing form is a big matrix aggregated by data points, has proved more useful when faced with sparsity. Based on the traditional co-clustering approaches, a new model named SC-DNMF, which takes into account the semantic constraints between words, is proposed in this paper. Experiments on several datasets indicate that our proposal improves the clustering accuracy over traditional co-clustering models.