Problem
Research questions and friction points this paper is trying to address.
depression symptoms
sentence-level recognition
diagnostic definitions
Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition
🤖 AI Summary
本文针对抑郁症症状识别中的表达相似性和定义不足问题，提出了一种两阶段框架，通过生成候选症状和基于定义的验证来提高识别准确性。
📝 Abstract
Sentence-level recognition of depression symptoms is challenging because similar expressions can differ in symptom relevance, and language-model inference is insufficiently grounded in diagnostic definitions. This study proposes a two-stage framework separating symptom-candidate generation from definition-grounded verification. A contrastively fine-tuned sentence encoder generates a symptom candidate per sentence, and a fine-tuned language model verifies whether the candidate is present or absent using the sentence, its context, and a candidate-specific diagnostic definition, checking its judgment against that definition before answering. Evaluated against encoder, inference-based, medical, and general LLM baselines and a matched single-stage supervised classifier, the proposed pipeline attains the best accuracy and F1 scores of all methods, with rationales matching expert-authored annotations. A preliminary clinical audit indicates moderate alignment with diagnostic definitions, with explanation quality strongly dependent on prediction correctness. The results support decomposing symptom recognition into candidate generation and definition-grounded verification, though performance remains limited for rare categories.
Innovation
Methods, ideas, or system contributions that make the work stand out.
two-stage framework
definition-grounded verification
contrastive fine-tuning
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