Abstract: As the basis of Chinese information processing,Chinese word segmentation(CWS) plays a very important role.To solve the problems of lacking of training samples and accessing a large number of labeled samples laboriously,a fresh active learning method based on nearest neighbor was proposed.The method adopts CRFs as the basic framework and uses the proposed active learning sampling strategy to select the most useful instances to annotate from a large number of unlabeled samples.Next the annotated are put instances into the labeled set and then the segmenter is trained by using the labeled set.Finally the method was tested in PKU corpora,MSR corpora and shanxi university corpora,and compared with the uncertainty sampling strategy.The experiment result shows that the fresh active learning selection strategy can select more valuable samples,reduce the cost of manual annotation effectively,and improve the accuracy of segmentation.

Key words: Chinese word segmentation,Active learning,Uncertainty sampling,Nearest neighbor rule