End-to-end relation extraction using lstms on sequences and tree structures

M Miwa, M Bansal - arXiv preprint arXiv:1601.00770, 2016 - arxiv.org
arXiv preprint arXiv:1601.00770, 2016arxiv.org
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 …
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.
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