Relation Extraction

The list of papers below cover the different research work that tackles relation extraction from neural network, attention mechanism, graph convolutional network, adversarial training, reinforcement learning, and other perspectives! Enjoy 🙂

  1. Neural knowledge acquisition via mutual attention between knowledge graph and text
  2. Attention-based bidirectional long short-term memory networks for relation classification
  3. Few-shot relation extraction via bayesian meta-learning on relation graphs
  4. Relation extraction with explanation
  5. Hybrid attention-based prototypical networks for noisy few-shot relation classification
  6. Relation extraction using supervision from topic knowledge of relation labels
  7. A hierarchical framework for relation extraction with reinforcement learning
  8. Long-tail relation extraction via knowledge graph embeddings and graph convolution networks
  9. Attention guided graph convolutional networks for relation extraction
  10. Discovering correlations between sparse features in distant supervision for relation extraction
  11. Cooperative denoising for distantly supervised relation extraction
  12. Neural relation extraction via inner-sentence noise reduction and transfer learning
  13. Robust distant supervision relation extraction via deep reinforcement learning
  14. Reinforcement learning for relation classification from noisy data
  15. Large scaled relation extraction with reinforcement learning
  16. DSGAN: Generative adversarial training for distant supervision relation extraction
  17. Graph convolution over pruned dependency trees improves relation extraction
  18. Hierarchical relation extraction with coarse-to-fine grained attention
  19. Modeling relational data with graph convolutional networks
  20. Deep residual learning for weakly-supervised relation extraction
  21. Adversarial training for relation extraction
  22. Distant supervision for relation extraction with sentence-level attention and entity descriptions
  23. Incorporating relation paths in neural relation extraction
  24. Jointly extracting relations with class ties via effective deep ranking
  25. Neural relation extraction with selective attention over instances
  26. Attention-based convolutional neural network for semantic relation extraction
  27. Bidirectional recurrent convolutional neural network for relation classification
  28. End-to-end relation extraction using lstms on sequences and tree structures
  29. Relation extraction with multi-instance multi-label convolutional neural networks
  30. Classifying relations via long short term memory networks along shortest dependency paths
  31. Distant supervision for relation extraction via piecewise convolutional neural networks
  32. Relation extraction: Perspective from convolutional neural networks
  33. Relation classification via convolutional deep neural network
  34. Distant supervision for relation extraction without labeled data
  35. Constructing biological knowledge bases by extracting information from text sources
Ryan

Ryan

Data Scientist

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