This paper presents a bilingual semi- supervised Chinese word segmentation (CWS) method that leverages the nat- ural segmenting information of English sentences. The proposed method in- volves learning three levels of features, namely, character-level, phrase-level and sentence-level, provided by multiple sub- models. We use a sub-model of condi- tional random fields (CRF) to learn mono- lingual grammars, a sub-model based on character-based alignment to obtain ex- plicit segmenting knowledge, and anoth- er sub-model based on transliteration sim- ilarity to detect out-of-vocabulary (OOV) words. Moreover, we propose a sub-model leveraging neural network to ensure the proper treatment of the semantic gap and a phrase-based translation sub-model to s- core the translation probability of the Chi- nese segmentation and its corresponding English sentences. A cascaded log-linear model is employed to combine these fea- tures to segment bilingual unlabeled data, the results of which are used to justify the original supervised CWS model. The eval- uation shows that our method results in su- perior results compared with those of the state-of-the-art monolingual and bilingual semi-supervised models that have been re- ported in the literature.