In this paper, we propose a neural network model for graph-based dependency parsing which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual information instead of using high-order factorization, and enable our model to use much fewer features than previous work. In addition, we propose an effective way to learn sentence segment embedding on sentence-level based on an extra forward LSTM network. Although our model uses only first-order factorization, experiments on English Peen Treebank and Chinese Penn Treebank show that our model could be competitive with previous higher-order graph-based dependency parsing models and state-of-the-art models.
REF used a similar model with a way to learn sentence segment embedding based on an extra forward LSTM network.
Graph-based Dependency Parsing with Bidirectional LSTM
ACL
Computer Science