  This paper reviews the development of Chinese word segmentation (CWS) in the
most recent decade, 2007-2017. Special attention was paid to the deep learning
technologies that has already permeated into most areas of natural language
processing (NLP). The basic view we have arrived at is that compared to
traditional supervised learning methods, neural network based methods have not
shown any superior performance. The most critical challenge still lies on
balancing of recognition of in-vocabulary (IV) and out-of-vocabulary (OOV)
words. However, as neural models have potentials to capture the essential
linguistic structure of natural language, we are optimistic about significant
progresses may arrive in the near future.
