A Feature-Rich CRF Segmenter for Chinese Micro-blog

This paper describes our system for Chinese word segmentation of micro-blog text, one of the NLPCC-ICCPOL 2016 Shared Tasks [1]. The CRF (Conditional Random Field) model is employed to model word segmentation as a sequence labeling problem, 7 sets of features are selected to train the CRF model. The system achieves f(b) 0.798144 on closed track, 0.81968 on semi-open track, and 0.82217 on open track with weighted measures [2].