Probabilistic Chinese word segmentation with non-local information and stochastic training

In this article, we focus on Chinese word segmentation by systematically incorporating non-local information based on latent variables and word-level features. Differing from previous work which captures non-local information by using semi-Markov models, we propose an alternative method for modeling non-local information: a latent variable word segmenter employing word-level features. In order to reduce computational complexity of learning non-local information, we further present an improved online training method, which can arrive the same objective optimum with a significantly accelerated training speed. We find that the proposed method can help the learning of long range dependencies and improve the segmentation quality of long words (for example, complicated named entities). Experimental results demonstrate that the proposed method is effective. With this improvement, evaluations on the data of the second SIGHAN CWS bakeoff show that our system is competitive with the state-of-the-art systems.

► We focus on word segmentation by incorporating non-local information based on latent variables and word-level features.
► For learning non-local information, we present an online training method with accelerated training speed.
► Evaluations on the SIGHAN data show that our system is competitive with the state-of-the-art systems.