This article proposes a unified, character-based, generative model to incorporate additional resources for solving the out-of-vocabulary (OOV) problem of Chinese word segmentation, within which different types of additional information can be utilized independently in corresponding submodels. This article mainly addresses the following three types of OOV: unseen dictionary words, named entities, and suffix-derived words, none of which are handled well by current approaches. The results show that our approach can effectively improve the performance of the first two types with positive interaction in F-score. Additionally, we also analyze reason that suffix information is not helpful. After integrating the proposed generative model with the corresponding discriminative approach, our evaluation on various corpora---including SIGHAN-2005, CIPS-SIGHAN-2010, and the Chinese Treebank (CTB)---shows that our integrated approach achieves the best performance reported in the literature on all testing sets when additional information and resources are allowed.