The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Open accessAbstract
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 treestructured 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 stateof-the-art feature-based model on end-toend relation extraction, achieving 12.1% and 5.7% relative error reductions in F1score 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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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
A survey of named entity recognition and classification
Nadeau, D., & Sekine, S. (2007) · Lingvisticae Investigationes · doi:10.1075/li.30.1.03nad · Cited by 2,507
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015) · arXiv · doi:10.48550/arxiv.1506.03099 · Cited by 1,621
Cited by
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Marcheggiani, D., & Titov, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1159 · Cited by 907
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Zhang, Y., Qi, P., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1244 · Cited by 847
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1113 · Cited by 787
Metadata
- Authors
- Miwa, M., & Bansal, M.
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 1105-1116
- OpenAlex
- W2964167098
- Cited by
- 1,322
- References
- 5
- FWCI
- 62.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
