We propose two completely neural network architectures for nested nested named entity recognition which do not explicitly build or model any structure and infer the relationships between nested NEs implicitly: • In the first model, we concatenate the nested entity multiple labels into one multilabel, which is then predicted with a standard LSTM-CRF (Lample et al., 2016) model.
sequence-to-sequence (seq2seq): The encoder is a bi-directional LsTM and the decoder is a LsTM.