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

Understand

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.