The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Open accessAbstract
Two problems arise when using distant supervision for relation extraction.First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data.However, the heuristic alignment can fail, resulting in wrong label problem.In addition, in previous approaches, statistical models have typically been applied to ad hoc features.The noise that originates from the feature extraction process can cause poor performance.In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems.To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account.To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features.Experiments show that our method is effective and outperforms several competitive baseline methods.
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Cited by
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
ERNIE: Enhanced Language Representation with Informative Entities
Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1139 · Cited by 1,482
Neural Relation Extraction with Selective Attention over Instances
Lin, Y., Shen, S., Liu, Z., Luan, H., & Sun, M. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1200 · Cited by 1,021
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yao, Y., Ye, D., Li, P., Han, X., Lin, Y., Liu, Z., Liu, Z., Huang, L., Zhou, J., & Sun, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1074 · Cited by 550
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Chen, X., Zhang, N., Xie, X., Deng, S., Yao, Y., Tan, C., Huang, F., Si, L., & Chen, H. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3511998 · Cited by 389
Metadata
- Authors
- Zeng, D., Liu, K., Chen, Y., & Zhao, J.
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 1753-1762
- OpenAlex
- W2251135946
- Cited by
- 1,149
- References
- 3
- FWCI
- 33.71
- Type
- conference paper
- Language
- en
- Open access
- bronze
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