Abstract: Semantic roles play an important role in the natural language understanding, but most of the existing semantic-role training datasets are relatively rough or even misleading in labeling semantic roles. In order to facilitate the fine-grained semantic analysis, an improved taxonomy of Chinese semantic roles is proposed by investigating a real-world corpus. Focusing on a corpus formed with sentences with only one pivotal semantic role, we propose a semi-automatic method for fine-grained Chinese semantic role dataset construction. A corpus of 9550 sentences has been labeled with 9423 pivot semantic roles, 29142 principal peripheral semantic roles and 3745 auxiliary peripheral semantic roles. Among them, 172 sentences are double-labeled with semantic roles and 104 sentences are labeled with semantic roles of uncertain semantic events. With a Bi-LSTM+CRF model, we compare the dataset against the Chinese Proposition Bank and reveal differences in the recognition of principal peripheral semantic roles, which provide clues for further improvement.