(2016), and use bidirectional LSTMs for both the character level and word level neural networks.
given an input sequence of words (w 1 , w 2 , ..., w n ), and each word's corresponding character sequence, the model first produces a representation for each word, x i , by concatenating its character representation with its word embedding.