However, for SAC, an accuracy score of 97.87% also means that 97.87% of   the words share common traits in which a majority of entity words are similar to the "True" tag vector, while the majority of non-entity words are similar to "False" tag vector.
because of the higher F1 score obtained from NeuralCRF+SAC, the two tag vectors can be viewed as features that are helpful for the NER task.