Conventional feature-rich parsers based on manually tuned features have achieved state-of-
the-art performance. However, these parsers are not good at handling long-term
dependencies using only the clues captured by a prepared feature template. On the other
hand, recurrent neural network (RNN)-based parsers can encode unbounded history
information effectively, but they perform not well for small tree structures, especially when
low-frequency words are involved, and they cannot use prior linguistic knowledge. In this …