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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 improveddoi:10.1016/j.ipm.2012.12.003 fatcat:2pznp54hu5ebzc7phb3y5hpeia