Distant supervised relation extraction based on residual attention

1. Mintz M, Bills S, Snow R, Jurafsky D. Distant supervision for relation extraction without labeled data. In: Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP. 2009, 1003–1011

2. Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X. Residual attention network for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 6450–6458

3. Asghar M Z, Subhan F, Ahmad H, Khan W Z, Hakak S, Gadekallu T R, Alazab M. Senti-eSystem: a sentiment-based eSystem-using hybridized fuzzy and deep neural network for measuring customer satisfaction. Software: Practice and Experience, 2021, 51(3): 571–594

   Google Scholar 

4. Reddy G T, Reddy M P K, Lakshmanna K, Kaluri R, Rajput D S, Srivastava G, Baker T. Analysis of dimensionality reduction techniques on big data. IEEE Access, 2020, 8: 54776–54788

   Article  Google Scholar 

5. Gadekallu T R, Rajput D S, Reddy M P K, Lakshmanna K, Bhattacharya S, Singh S, Jolfaei A, Alazab M. A novel PCA-whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. Journal of Real-Time Image Processing, 2021, 18(4): 1383–1396

   Article  Google Scholar 

6. Lin Y, Shen S, Liu Z, Luan H, Sun M. Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016, 2124–2133

7. Huang Y, Wang W Y. Deep residual learning for weakly-supervised relation extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017, 1803–1807

8. Li X, Chen Y, Xu J, Zhang Y. Distant supervision for relation extraction with gate mechanism. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(1): 39–44

   Google Scholar 

9. Li H, Liu Y J, Xie Q, Tang L L. Distant supervision relation extraction model based on multi-level attention mechanism. Computer Science, 2019, 46(10): 252–257

   Google Scholar 

Download references