We introduce a generative probabilistic document model based on latent Dirichlet allocation (LDA), to deal with textual errors in the document collection. Our model is inspired by the fact that most large-scale text data are machine-generated and thus inevitably contain many types of noise. The new model, termed as TE-LDA, is developed from the traditional LDA by adding a switch variable into the term generation process in order to tackle the issue of noisy text data. Through extensive experiments, the efficacy of our proposed model is validated using both real and synthetic data sets