Class-Missing Semi-supervised document key information extraction via synergistic refinement estimation
Bibliographic record
Abstract
Current methods for document key information extraction (DKIE) rely heavily on labeled data with high annotation costs. To mitigate this issue, the semi-supervised learning (SSL) paradigm, which utilizes unlabeled document samples, has gained broad attention in DKIE. However, existing SSL methods require labeled and unlabeled data to share an identical label space, which is impractical in many DKIE tasks (i.e., some unlabeled samples do not belong to any known classes in the labeled set). In this paper, we formulate this problem as Class-Missing Semi-supervised (CMSS) DKIE. In DKIE, unknown classes usually belong to minority and fine-grained categories, intensifying the misconnections between known and unknown classes and making CMSS more challenging. To address this issue, we propose Synergistic Refinement Estimation (SRE), a progressive prototype estimation scheme that alleviates the unknown classes bias to the majority known classes on long-tailed unlabeled data. Furthermore, dynamic threshold hash rectification and structural calibration mechanisms are proposed to correct connections between fine-grained classes. Extensive experimental results demonstrate that SRE surpasses existing state-of-the-art methods on several DKIE benchmarks. Code is available at https://github.com/anonymoulink/SRE_DKIE .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".