In the process of developing a domain-specific Chinese-English machine translation system, the accuracy of Chinese word segmentation on large amounts of training text often decreases because of unknown words. The lack of domain-specific annotated corpus makes supervised learning approaches unable to adapt to a target domain. This problem results in many errors in trans- lation knowledge extraction and therefore seriously lowers translation quality. To solve the domain adaptation problem, we implement Chinese word segmen- tation by exploring n-gram statistical features in large Chinese raw corpus and bilingually motivated Chinese word segmentation, respectively. Moreover, we propose a method of combining multiple Chinese word segmentation results based on linear model to augment domain adaptation. For evaluation, we con- duct experiments of Chinese word segmentation and Chinese-English machine translation using the data of NTCIR-10 Chinese-English patent task. The expe- rimental results showed that the proposed method achieves improvements in both F-measure of the Chinese word segmentation and BLEU score of the Chinese-English statistical machine translation system.
['Chen Su', 'Yujie Zhang', 'Zhen Guo', 'Jinan Xu']
Exploring Multiple Chinese Word Segmentation Results Based on Linear Model