Joint Constrained Learning for Causal Event-Event Relation Extraction of Brain Connectome
Bibliographic record
Abstract
Brain science research has entered the era of con-nectome. The era of connectome has brought new challenges and opportunities for brain science research. Many studies have reported the structural and functional connections of the brain, but extracting scientific evidences from them is not easy. Traditional neuroimaging text mining methods based on terms are not suitable for the complex experimental designs and results analysis of brain connectome studies. Therefore, this paper proposes a novel method for event-level neuroimaging text mining, which aims to extract causal event-event relations of brain connectome. The method uses a deep learning model that combines BiLSTM and MLP, and incorporates constraints learning to enhance the model's performance on few-shot datasets. The constraints include common sense constraints and domain constraints, which help the model to learn from prior knowledge and domain expertise. The experiments on a brain connectome article dataset show that the proposed method can effectively extract the causal event-event relations of brain connectome with low resource requirements.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".