Anaphoric reference is an important linguistic phenomenon to understand the discourse structure and content. In Chinese natural language processing, there are both the problems of choosing and resolving anaphora. In Mandarin Chinese, several linguists have attempted to propose criteria to explain the phenomenon of anaphora but with controversial results. On the other hand, search-based computational techniques for resolving anaphora are neither the best way to resolve Chinese anaphora nor to facilitate choosing anaphora. Thus, to facilitate both choosing and resolving anaphora with accuracy and efficiency, we propose a case-based learning model G-UNIMEM to automatically acquire anaphoric regularity from a sample set of training sentences, which are annotated with a list of features. The regularity acquired from training was then tested and compared with other approaches in both choosing and resolving anaphora.