Ontology-Driven Structural Regularization for Document-Level Relation Extraction
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Computer Science > Computation and Language
Title:Ontology-Driven Structural Regularization for Document-Level Relation Extraction
Abstract:Document-Level Relation Extraction (DocRE) relies heavily on costly manually annotated datasets, while large distant supervision resources such as DocRED distant remain underexploited due to noise. We show that a critical yet overlooked source of noise lies in structural inconsistencies within relational triples, including violations of ontology constraints and logical contradictions.
We introduce an ontology-driven framework to quantify and enforce structural consistency in DocRE datasets. Our analysis reveals substantial structural noise in DocRED distant and demonstrates that such inconsistencies propagate to model predictions. Enforcing structural well-formedness during training significantly reduces logical contradictions and consistently improves generalization performance. These findings establish structural consistency as a missing axis of supervision in DocRE and highlight structural regularization as an effective strategy for leveraging distant data at scale.
| Comments: | Accepted at EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.20856 [cs.CL] |
| (or arXiv:2608.20856v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.20856
arXiv-issued DOI via DataCite (pending registration)
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