Semi-supervised Chinese Word Segmentation based on Bilingual Information

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.