Chinese word structure annotation is potentially useful for many NLP tasks, especially for Chinese word segmentation. Li and Zhou (2012) have presented an annotation for word structures in the Penn Chinese Treebank. But they only consider words that have productive affixes, which covers 35% of word types in that corpus. In this paper, we propose a linguistically inspired annotation that covers various morphological derivations of Chinese in a more general way, such that almost all multiple-character words can be structurally analyzed. As manual annotation is expensive, we propose a semi-supervised approach to automatic annotation, which combines the maximum entropy learning and the EM iteration for the Gaussian mixture model. The proposed method has achieved an accuracy of 90% on the testing set. © 2021 CLP 2012 - 2nd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All Rights Reserved.