  Chinese word segmentation (CWS) is a fundamental step of Chinese natural
language processing. In this paper, we build a new toolkit, named PKUSEG, for
multi-domain word segmentation. Unlike existing single-model toolkits, PKUSEG
targets multi-domain word segmentation and provides separate models for
different domains, such as web, medicine, and tourism. Besides, due to the lack
of labeled data in many domains, we propose a domain adaptation paradigm to
introduce cross-domain semantic knowledge via a translation system. Through
this method, we generate synthetic data using a large amount of unlabeled data
in the target domain and then obtain a word segmentation model for the target
domain. We also further refine the performance of the default model with the
help of synthetic data. Experiments show that PKUSEG achieves high performance
on multiple domains. The new toolkit also supports POS tagging and model
training to adapt to various application scenarios. The toolkit is now freely
and publicly available for the usage of research and industry.
