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 captures both word sequence and dependency tree substructure information by stacking bidirectional tree structured LSTM RNNs on bidirectional sequential LSTM RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state of the art feature based model on end to end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1 score on ACE2005 and ACE2004, respectively. We also show that our LSTM RNN based model compares favorably to the state of the art CNN based model (in F1 score) on nominal relation classification (SemEval 2010 Task 8) Finally, we present an extensive ablation analysis of several model components.
ACL