DIAG-NRE: A Neural Pattern Diagnosis Framework forDistantly Supervised Neural Relation Extraction
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 highquality 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. Source codes and
data can be found at https://github.
com/thunlp/DIAG-NRE.