%0 Generic %1 miwa2016endtoend %A Miwa, Makoto %A Bansal, Mohit %D 2016 %K deep_learning lstm relex rnn %T End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures %U http://arxiv.org/abs/1601.00770 %X 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.

@misc{miwa2016endtoend, abstract = {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.}, added-at = {2018-02-22T11:28:00.000+0100}, author = {Miwa, Makoto and Bansal, Mohit}, biburl = {https://www.bibsonomy.org/bibtex/29b4a0cdc94f96521535d1901cb136800/dallmann}, description = {1601.00770.pdf}, interhash = {a31c3fa7e26048ce288d8af72c2dc6c3}, intrahash = {9b4a0cdc94f96521535d1901cb136800}, keywords = {deep_learning lstm relex rnn}, note = {cite arxiv:1601.00770Comment: Accepted for publication at the Association for Computational Linguistics (ACL), 2016. 13 pages, 1 figure, 6 tables}, timestamp = {2018-02-22T11:28:00.000+0100}, title = {End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures}, url = {http://arxiv.org/abs/1601.00770}, year = 2016 }