(2016) and apply the bidirectional long short-term memory (Hochreiter and Schmidhuber, 1997) (BiLSTM) networks as the neural architecture for all baselines and our approaches.
we implement the following baselines: a Simple model which is a linearchain LSTM-CRF model and we treat all missing labels as O; the missing label CRF (Bellare and McCallum, 2007;Greenberg et al., 2018) (LSTM-M-CRF) model; the partial perceptron (Carlson et al., 2009) model, which is a structured perceptron (Collins, 2002) but only considers the scores on the words with available labels; the transductive perceptron (Fernandes and Brefeld, 2011) model where they introduce a Hamming loss function during the perceptron training process; lastly, we train an LSTM-CRF (Lample et al., 2016) with complete annotations as the upper bound (Com-plete).