Abstract
Few-Shot Relation Extraction is a challenging task that involves extracting relations from a limited number of annotated data. While some researchers have proposed using sentence-level information to improve performance on this task with Prototype Network, most of these methods do not adequately leverage this valuable source of sentence-level information. To address this issue, we propose a novel sentence augmentation method that utilizes abundant relation information to generate additional training data for few-shot relation extraction. In addition, we add a new “None of Above” class for each task, thereby enhancing the model’s classification ability for similar relations and improving its overall performance. Experimental results on the FewRel demonstrate that our method outperforms existing methods on three different few-shot relation extraction tasks. Moreover, our method also provides a new idea for both few-shot learning and data augmentation research.
This work is supported by National Natural Science Foundation of China (No. 61972414), National Key R &D Program of China (No. 2019YFC0312003) and Beijing Natural Science Foundation (No. 4202066).
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Wang, T., Wang, Z., Wang, R., Li, D., Lu, Q. (2023). Contextual Information Augmented Few-Shot Relation Extraction. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_13
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