To represent each input token, we use four vectors: a learned word embedding w; a fixed word embedding w; a learned POS-tag embedding p; a hand-crafted feature representation d. The four vectors are concatenated together, transformed by a matrix V and fed to a rectified layer to learn feature combination, as where [ w; w; p; d] means the concatenation.
the sequence tagging methods and RNN method have no power of modeling the linguistic structural integrity.