Motivated by the recent success of deep architectures in general and deep recurrent networks in particular, we explore an application of deep bidirectional RNNshenceforth deep RNNs -to the task of opinion expression extraction.
another way to incorporate information about the future is to add bidirectionality to the architecture, referred as the bidirectional RNN (Schuster and Paliwal, 1997): where  In this setting − → h t and ← − h t can be interpreted as a summary of the past, and the future, respectively, around the time step t. When we make a decision on an input vector, we employ the two intermediate representations − → h t and ← − h t of the past and the future.