End-to-end Relation Extraction using LSTMs on Sequences and Tree Structures

We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model stacks bidirectional sequential LSTM-RNNs and bidirectional tree-structured LSTM-RNNs to capture both word sequence and dependency tree substructure information. This allows our model to jointly represent both entities and relations with shared parameters. We further encourage detection of entities during training and use of entity information in relation extraction via curriculum learning and scheduled sampling. Our model improves over the state-of-the-art feature-based model on end-to-end relation extraction, achieving 3.5% and 4.8% relative error reductions in F-score on ACE2004 and ACE2005, respectively. We also show improvements over the state-of-the-art convolutional neural network based model on nominal relation classification (SemEval-2010 Task 8), with 2.5% relative error reduction in F-score.