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• W2953326415 abstract "We present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a KB. By explicitly reasoning about missing data during learning, our approach enables large-scale training of 1D convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model." @default.
• W2953326415 created "2019-06-27" @default.
• W2953326415 creator A5000388943 @default.
• W2953326415 creator A5039096905 @default.
• W2953326415 date "2019-03-29" @default.
• W2953326415 modified "2023-09-27" @default.
• W2953326415 title "Structured Minimally Supervised Learning for Neural Relation Extraction" @default.
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• W2953326415 hasPublicationYear "2019" @default.
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