As the authors have mentioned in their papers, for a given sub-structure in their model, there exist multiple ways of interpreting the combination of mentions.
the final representation for i-th word is the concatenation of them: Next, we use the a bidirectional LStM to capture contextspecific information for each word, resulting in the word-level features: Such representations are then used as inputs to a second LStM to generate span-level features: Inspired by (Kong et al., 2016), we compute all possible span embeddings efficiently with time complexity O(cn) using dynamic programming, with n being the number of words in the input x and c being the maximal length of a mention.