Semi-supervised Chinese Word Segmentation based on Bilingual Information

This paper presents a bilingual semisupervised Chinese word segmentation (CWS) method that leverages the natural segmenting information of English sentences. The proposed method involves learning three levels of features, namely, character-level, phrase-level and sentence-level, provided by multiple submodels. We use a sub-model of conditional random fields (CRF) to learn monolingual grammars, a sub-model based on character-based alignment to obtain explicit segmenting knowledge, and another sub-model based on transliteration similarity 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 score the translation probability of the Chinese segmentation and its corresponding English sentences. A cascaded log-linear model is employed to combine these features to segment bilingual unlabeled data, the results of which are used to justify the original supervised CWS model. The evaluation shows that our method results in superior results compared with those of the state-of-the-art monolingual and bilingual semi-supervised models that have been reported in the literature.