Corpus ID: 226965445

Zero-shot Learning for Relation Extraction

@article{Gong2020ZeroshotLF,
  title={Zero-shot Learning for Relation Extraction},
  author={Jiaying Gong and H. Eldardiry},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.07126}
}
  • Jiaying Gong, H. Eldardiry
  • Published 2020
  • Computer Science
  • ArXiv
  • Most existing supervised and few-shot learning relation extraction methods have relied on labeled training data. However, in real-world scenarios, there exist many relations for which there is no available training data. We address this issue from the perspective of zero-shot learning (ZSL) which is similar to the way humans learn and recognize new concepts with no prior knowledge. We propose a zero-shot learning relation extraction (ZSLRE) framework, which focuses on recognizing novel… CONTINUE READING

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    References

    SHOWING 1-10 OF 47 REFERENCES
    Neural Snowball for Few-Shot Relation Learning
    • 11
    • PDF
    Modeling Relations and Their Mentions without Labeled Text
    • 809
    • Highly Influential
    • PDF
    Few-shot relation classification by context attention-based prototypical networks with BERT
    • 1
    ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
    • 9
    • PDF
    One-shot learning for fine-grained relation extraction via convolutional siamese neural network
    • 8
    Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
    • 49
    • Highly Influential
    • PDF
    FewRel: A Large-Scale Supervised Few-shot Relation Classification Dataset with State-of-the-Art Evaluation
    • 102
    • Highly Influential
    • PDF
    Zero-Shot Transfer Learning for Event Extraction
    • 50
    • PDF