Problem
Research questions and friction points this paper is trying to address.
Suicide Risk Assessment
Natural Language Processing
Class Imbalance
Clinical Factors
Social Media Text
Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment
๐ค AI Summary
็ ็ฉถ้่ฟไปปๅก็ฅ่ฏๅๅฐ่ชๆ้ฃ้ฉ่ฏไผฐๆจกๅๅคๆๆง๏ผ้็จ31็งๆๆฏ่ฟ่ก300ๆฌกๅฎ้ช๏ผๆ็ป็ณป็ปๅจ้ฃ้ฉใ่ฏๆฎๅๅ ็ด ้ขๆตไธๅๅพๆพ่ๆๆใ
๐ Abstract
Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidence and clinically relevant risk and protective factors. Yet common NLP techniques, including model scaling, synthetic data, loss reweighting, ensembling, and threshold tuning, are often applied without testing whether their gains hold up under severe class imbalance, coupled outputs, and limited author-level data. We study 1,635 clinician-annotated posts and audit 31 pre-specified techniques from 7 methodological families through roughly 300 controlled experiments on author-disjoint partitions. We found no prior audit of this playbook in this regime. The findings guide a task-grounded system for three outputs: 4-level suicide risk, evidence spans, and 24 clinical risk and protective factors. Only 5 of 31 comparisons produced reliable gains. We reformulate factor prediction as entailment between each post and its codebook definitions, using an architecturally diverse ensemble with class-balanced training and score rescaling. Risk predictions condition a 7-model evidence tagger ensemble; evidence restricts symbolic risk rules; and a difficult risk class is routed separately. The factor predictor remains independent because risk evidence provides no additional factor signal. We also correct a mismatch between validation scores used for threshold fitting and test-time ensemble scores through deployment-consistent calibration, yielding the largest improvement to the factor system. The final system achieves 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, with a 0.7781 composite, ranking third among 53 teams. We call the underlying principle task-conditioned technique selection: retain techniques only when task-specific knowledge, structure, or empirical evidence justifies them.
Innovation
Methods, ideas, or system contributions that make the work stand out.
task-conditioned technique selection
entailment formulation
ensemble model
deployment-consistent calibration
class-balanced training
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