In this study, we specifically examine BLSTMCNNs-CRF<cite> (Ma and Hovy, 2016)</cite> because it achieves state-of-the-art performance in the CoNLL 2003 corpus. Figure 1 presents the architecture of this model. This word-based model combines CNN, BLSTM, and CRF layers. We describe each layer of this model as the following. CNN Layer: This layer is aimed at extracting subword information. The inputs are character embeddings of a word. This layer consists of convolution and pooling layers. The convolution layer produces a matrix for a word with consideration of the sub-word. The pooling layer compresses the matrix for each dimension of character embedding. BLSTM Layer: BLSTM (Graves and Schmidhuber, 2005 ) is an approach to treat sequential data. The output of CNN and word embedding are concatenated as an input of BLSTM. CRF Layer: This layer was designed to select the best tag sequence from all possible tag sequences with consideration of outputs from BLSTM and correlations between adjacent tags. This layer introduces a transition score for each transition pattern between adjacent tags. The objective function is calculated using the sum of the outputs from BLSTM and the transition scores for a sequence.