At the core of both of these components is the issue of span representation.
given that a span's correct label and its quality as a constituent depend heavily on the context in which it appears, we naturally turn to recurrent neural networks as a starting point, since they have previously been shown to capture contextual information suitable for use in a variety of natural language applications (Bahdanau et al., 2014;Wang et al., 2015) we run a bidirectional LSTM over the input to obtain context-sensitive forward and backward encodings for each position i, denoted by f i and b i , respectively.