Similar to<cite> Krishnan and Manning (2006)</cite> , we also encode non-local information with features and apply the simple two-stage architecture. Different from their work for improve label consistency, their features are activated on the recognized entities coming from the first CRF, the non-local features we design are used to recall more missed entities which are seen in the training data or unseen entities but some of their occurrences being recognized correctly in the first stage, our features are fired on the raw token sequence directly with forward maximum match. Compared to their non-local information extracted from training data with 10-fold cross-validation, our non-local information is extracted from the training date directly; our approach obtaining the non-local features is simpler. Moreover, we design different non-local features encoding different useful information for NER two subtasks: entity boundary detection and entity semantic classification.