Improving feature-rich transition-based constituent parsing using recurrent neural networks

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 paper, we propose a simple but effective framework to combine the merits of feature-rich transition-based parsers and RNNs. Specifically, the proposed framework incorporates RNN-based scores into the feature template used by a feature-rich parser. On English WSJ treebank and SPMRL 2014 German treebank, our framework achieves state-of-The-Art performance (91.56 F-score for English and 83.06 F-score for German), without requiring any additional unlabeled data.