A Noise-Weighted Unsupervised Denoising Approach for Distant Supervision Relation Extraction

Distant supervision relation extraction (DS-RE) provides an efficient way to construct large-scale training data by automatically aligning knowledge base relations with unstructured texts. However, this process inevitably introduces erroneous labels because sentences containing the same entity pair do not always express the corresponding knowledge base relations. To address this problem, this paper proposes a noise-weighted unsupervised denoising framework that integrates sentence-level prior confidence estimation, multi-factor representation learning, fine-grained noise detection, and clustering-based label generation. The framework first estimates noise-aware prior weights by matching sentence instances with semantically similar relation triples. It then incorporates lexical, positional, and entity-type factors to enhance sentence representations. For detected noisy instances, an unsupervised clustering-based label generation module is used to regenerate relation labels rather than directly discarding them. Experimental results on the DSRED dataset show that the proposed method achieves 89.7% Precision, 90.6% Recall, 90.1% F1-score, and a PR-AUC of 0.942±0.004, outperforming the strongest baseline EFEAPN by 1.7 percentage points in F1-score. Statistical analysis further shows that the PR-AUC improvement remains significant after Bonferroni correction (padj=0.0094). Module-level ablation experiments, sensitivity analysis, and clustering quality evaluation further verify the effectiveness of the noise weighting and clustering-based label generation modules. Supplementary experiments with Transformer-based encoders and cross-dataset evaluation further show that the main performance gain comes from the proposed denoising framework rather than from a specific sentence encoder.