Document-Based Relation Filtering

• Document-based relation filtering is a set of techniques designed to identify and extract meaningful entity and document relationships, addressing challenges like combinatorial explosion and multi-hop reasoning.
• It leverages graph-based modeling, multimodal integration, and query-driven unsupervised methods to effectively filter out spurious or irrelevant candidate relations.
• This approach enhances applications in knowledge base construction, semantic search, and large-scale document organization by reducing noise and improving interpretability.

Document-based relation filtering encompasses a broad class of techniques for identifying, extracting, or constructing relationships either among entities within documents or among documents themselves, with a central goal of determining which candidate relations are relevant and which are spurious or irrelevant. Across machine learning, natural language processing, and information retrieval, relation filtering at the document level is motivated by the need to sift through large combinatorial spaces—be it entity pairs or document pairs—so as to return precise, robust, and interpretable relational structures for downstream applications such as knowledge base construction, semantic search, or information organization.

1. Foundations and Problem Setting

Document-based relation filtering arises from the recognition that real-world documents encode vast numbers of pairwise (or higher-order) candidate relationships, of which only a small subset typically correspond to meaningful facts or connections. In the context of entity-based extraction, document-level relation extraction (DocRE) was developed to move beyond the limitations of sentence-level extraction: whereas sentence-level RE assumes all relation evidence is local, DocRE must aggregate and reason over entity mentions and supporting facts dispersed over many sentences or even paragraphs (Delaunay et al., 2023). This brings new challenges, including:

• Combinatorial explosion of candidate pairs, especially in long documents or those with many entities.
• The predominance of "no relation" (NA) instances, leading to extremely imbalanced label distributions (Popovic et al., 2022, Choi et al., 2023, Li et al., 25 Aug 2024).
• Requirement for robust multi-hop and cross-sentence reasoning, often involving co-reference resolution and logical aggregation.
• In graph-based and knowledge-driven approaches, the lack of a gold "relational graph" outside of supervised settings, making prior-graph design or filtering a core concern (Qi et al., 2022, Jain et al., 22 Jan 2024).

A related but distinct use case is the discovery of relationships among documents themselves (e.g., document clustering, evidence chains, cross-domain linkages), a setting that has traditionally relied on similarity-based methods but has more recently advanced to probabilistic co-retrieval and marginalization techniques (Iwamoto et al., 14 Jul 2025).

2. Filtering Strategies in Entity-based Relation Extraction

2.1 Graph-based Modeling and Path Reasoning

Many state-of-the-art DocRE models utilize global graph representations where nodes correspond to entity mentions, entities, or sentences, and edges encode syntactic, semantic, or discourse-based links. Filtering in this context is both implicit (through network design and attention mechanisms) and explicit (via dedicated modules):

• In "Document-Level Relation Extraction with Reconstruction" (Xu et al., 2020), a reconstructor module is trained to reconstruct ground-truth dependency paths between entities using an LSTM decoder, introducing a path-based filtering signal. The model’s joint loss penalizes incorrect path generation, ensuring that only entity pairs with strong evidence paths receive high confidence.
• The "Discriminative Reasoning for Document-level Relation Extraction" framework (Xu et al., 2021) decomposes relation classification into distinct reasoning paths (intra-sentence, logical, and coreferential). By aggregating over the maximal score across these reasoning types, the model selectively filters out candidate relations lacking robust, multi-view support.
• The SIEF method (Xu et al., 2022) quantifies the evidential impact of each sentence using a sentence importance score; a focusing loss ensures that non-evidence sentences do not artificially sway predictions, making models robust to input perturbation and better focused on the core evidence.

2.2 Multi-modal and Layout-aware Filtering

In visually-rich documents, relation filtering must integrate visual, spatial, and textual modalities. The LayoutLMv3-based model (Adnan et al., 16 Apr 2024) fuses text, layout (bounding boxes), and image features, predicting entity-to-entity relations via a bilinear parameter to compute relation scores for each entity pair. Techniques such as bounding box ordering are introduced to regularize the spatial context, improving the model’s ability to filter misaligned or spurious entity links especially in noisy OCR settings.

3. Handling Imbalanced and No-Relation Instances

The overwhelming prevalence of NA label candidates in document-level settings introduces substantial filtering complexity. Recent approaches address this through loss functions, adaptive thresholding, and candidate proposal modules:

• Few-shot DocRE benchmarks such as FREDo (Popovic et al., 2022) and studies of NOTA distributions show that real-world settings can have >96% candidate pairs without valid relations. Models adapted for this include class-adaptive loss functions and the explicit inclusion of NOTA prototypes.
• PRiSM (Choi et al., 2023) improves filtering under low-resource regimes by calibrating the output probabilities using both bilinear scores and semantic similarity between entity pairs and relation descriptions; this dual-path scoring reduces overconfident NA predictions and enhances detection of rare, valid relations.
• Classifier-LLM approaches (Li et al., 25 Aug 2024) propose a pre-filtering stage whereby a specific classifier narrows the entity pair set to plausible candidate pairs before passing these to an LLM for final classification, thus focusing attention and mitigating the negative impact of NA dispersion.

4. Relation Correlation and Co-occurrence-based Filtering

Multi-label and long-tail settings in DocRE invite the modeling of correlation structures among relations:

• Explicit modeling of relation correlation graphs (Huang et al., 2023, Han et al., 2022) uses statistical co-occurrence (conditional probability, e.g., $P(L_b|L_a)$) among relation labels in training data to construct a relation graph, over which embeddings are aggregated via GAT layers. The approach enhances filtering by suppressing unlikely or mutually exclusive relation predictions and transferring robustness from frequent to rare relations.
• Auxiliary co-occurrence prediction tasks (coarse- and fine-grained) provide additional losses that encourage models to learn correlations; these correlation-aware representations are then used to update entity pair features prior to the final prediction (Han et al., 2022).

5. Weak and Unsupervised Supervision in Filtering

As hand-annotated training data is resource intensive, weak supervision and unsupervised query-driven approaches have been proposed:

• The PromptRE framework (Gao et al., 2023) employs multiple prompting strategies—relation-specific, open-ended, and existence verification—aggregated by data programming to produce denoised and filtered pseudo-labels, with further refinement by type-compatibility priors.
• In the field of document-level relationships (as distinct from intra-document entity relations), EDR-MQ (Iwamoto et al., 14 Jul 2025) proposes unsupervised filtering by marginalizing over user queries: the MC-RAG module estimates joint document probabilities $p(z_i, z_j)$ across a large set of queries, such that documents that co-recur across diverse queries are inferred to be strongly related. This method reveals topical clusters, evidence chains, and cross-domain links that escape traditional similarity-based retrieval.

Filtering Technique	Core Mechanism	Filtering Effect
Path reasoning (DocRE)	Explicit reasoning paths/meta-paths	Filters by multi-hop evidential strength
Correlation graphs	Relation co-occurrence/conditional probability	Suppresses unlikely multi-label combinations
Candidate proposal (LLM)	Classifier pre-filtering of entity pairs	Narrows candidate pool before final classification
Masked image modeling	Matrix reconstruction via self-attention	Filters noise, leverages inter-pair correlation
Query marginalization	Aggregation over diverse user queries	Reveals cross-document clusters, links, and chains

6. Practical Applications and System Integration

Document-based relation filtering plays a vital role in several application scenarios:

• Adaptive document filtering systems (such as news, legal, or biomedical information feeds) benefit from fine-grained, feature-informed filtering of relevant content (Zhang et al., 2014).
• Large-scale knowledge base construction relies on accurate relation filtering to minimize propagation of spurious facts from extracted candidate sets (Delaunay et al., 2023).
• Visual document understanding (e.g., invoice and receipt parsing) is improved by multi-modal filtering that leverages visual-spatial cues, dramatically enhancing downstream automation processes (Adnan et al., 16 Apr 2024).
• Query-driven or user-facing corpus exploration tools employ unsupervised filtering methods to enable dynamic discovery of document relations tailored to user perspectives (Iwamoto et al., 14 Jul 2025).
• Weak supervision via prompt aggregation or marginalization allows for practical deployment in settings with limited or no annotated data (Gao et al., 2023).

7. Future Directions and Open Research Problems

Recent progress underscores the importance and complexity of document-based relation filtering, yet several open challenges and avenues for future research remain:

• Further refinement and dynamic modeling of relation correlations, possibly with hierarchical or context-conditioned graphs, can address remaining long-tail and polysemy issues (Han et al., 2022, Huang et al., 2023).
• Improvements in weakly supervised and unsupervised filtering, especially for settings with limited labels, depend on advances in prompt engineering, data programming, and query selection (Gao et al., 2023, Iwamoto et al., 14 Jul 2025).
• Scaling graph-based and multi-modal filtering techniques to longer and more complex documents, as well as cross-document and cross-modal settings, requires both algorithmic innovation and computational efficiency (Qi et al., 2022, Delaunay et al., 2023).
• Bridging the gap between highly specialized relation extraction models and the adaptivity/flexibility of LLM approaches (especially through modular or cascade architectures) stands as a critical frontier (Li et al., 25 Aug 2024).
• More interpretable and transparent filtering, leveraging path-based or traversal explanations, will be essential for high-stakes applications in scientific, legal, and compliance domains (Jain et al., 22 Jan 2024).

In sum, document-based relation filtering is a multifaceted research area spanning supervised, weakly supervised, and unsupervised settings, with methodologies grounded in reasoning, feature modeling, graph theory, and query-driven discovery. Continued progress is likely to blend these dimensions, advancing robust, scalable, and interpretable filtering in increasingly complex and application-rich environments.