In order to establish the long dependencies easily and overcome the disadvantage of the approximate inference,<cite> Krishnan and Manning (2006)</cite> propose a two-stage approach using Conditional Random Fields (CRFs) with extract inference. They represent the non-locality with non-local features, and extract the nonlocal features from the output of the first stage CRF using local context alone; then they incorporate the non-local features into the second CRF. But the features in this approach are only used to improve label consistency. To our best knowledge, up to now, non-local information has not been explored to improve NER recall in previous researches; on the other hand, NER is always impaired by its lower recall due to the imbalanced distribution where the NONE class dominates the entity classes.