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- W2948654710 abstract "Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these methods require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly.To ease the labor-intensive workload of pattern writing and enable the quick generalization to new relation types, we propose a neural pattern diagnosis framework, DIAG-NRE, that can automatically summarize and refine high-quality relational patterns from noise data with human experts in the loop. To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods." @default.
- W2948654710 created "2019-06-14" @default.
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- W2948654710 date "2018-11-06" @default.
- W2948654710 modified "2023-10-04" @default.
- W2948654710 title "DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction" @default.
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- W2948654710 doi "https://doi.org/10.48550/arxiv.1811.02166" @default.
- W2948654710 hasPublicationYear "2018" @default.
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