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Pattern-based labeling methods have achieved promising results in alleviatingthe inevitable labeling noises of distantly supervised neural relationextraction. However, these methods require significant expert labor to writerelation-specific patterns, which makes them too sophisticated to generalizequickly.To ease the labor-intensive workload of pattern writing and enable thequick generalization to new relation types, we propose a neural patterndiagnosis framework, DIAG-NRE, that can automatically summarize and refinehigh-quality relational patterns from noise data with human experts in theloop. To demonstrate the effectiveness of DIAG-NRE, we apply it to tworeal-world datasets and present both significant and interpretable improvementsover state-of-the-art methods.

DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction