However, since the expanded gazetteers cover significant proportions of the entities in the training data, the weight of gazetteers features are easily inflated and thus the model tends to rely too much on lexical features extracted from the gazetteers fea-tures to assign a tag rather than the contextual features such as n-gram, a phenomenon called "feature under-training".
we often observe noticeable performance degradation at test time when the entity value does not exist in the training set or the entity dictionary.