Community Reading: CoRE-NEPD for Cross-Document RE

Community research spotlight

We did not author this paper. We're sharing it because it is relevant to graph data, information extraction, and the problems Curate Labs studies.

Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing targets relation extraction where evidence is distributed across multiple documents.

The paper argues that prior cross-document systems over-focus on bridge entities while ignoring non-bridge entities that still provide semantic association. It also addresses prediction bias caused by many NA instances in CodRED.

Why it matters

The proposed system builds a unified entity graph over target, bridge, and non-bridge entities, then uses a graph recurrent network plus debiasing. That combination is interesting because it improves both evidence representation and decision calibration.

The paper reports state-of-the-art results on CodRED closed and open settings and releases code.

Our community read

This is one of the most directly relevant patterns for multi-document intelligence, literature discovery, and dossier-style extraction. Those systems live or die by whether evidence routing works.

The limitation is that operational cross-document IE requires more than relation modeling. Retrieval, document bag construction, deduplication, provenance, and false-positive control all become part of the product surface.

Source

• arXiv: 2406.16529