The main idea is to replace manual discrete features with automatic real-valued features, which are derived automatically from distributed character representations using neural networks.
convolution neural network 1 (Zheng et al., 2013), tensor neural network (Pei et al., 2014), recursive neural network (Chen et al., 2015a) and longshort-term-memory (LSTM) (Chen et al., 2015b) have been used to derive neural feature representations from input word sequences, which are fed into a CRF inference layer.