arXiv cs.CL· Sumanth Manduru, Carlotta Domeniconi·· 10 小时前AI 评分22
SREE：从相关帖子到可归因文本片段，为抑郁问卷推断提供明确症状证据
From Relevant Posts to Attributable Spans: Explicit Symptom Evidence for Depression Questionnaire Inference
AI 导读
SREE 是一个证据驱动的流水线，先抽取第一人称、症状特异的文本片段，再用受 Facility Location 启发的目标函数选出代表性子集，并在所选证据上执行 Evidence-Gated Inference。
正文 · 原文
Abstract:Longitudinal social-media histories offer an opportunity to assess depression, potentially supporting the inference of standardized depression questionnaire responses. However, this task requires evidence that is semantically relevant to a symptom and attributable to the user, while being sufficiently specific to support an individual questionnaire item. Existing approaches rely on semantic similarity to retrieve posts, which can introduce misleading or non-attributable evidence, particularly in multi-symptom posts and discussions of others' experiences. We propose SREE (Symptom Retrieval and Evidence-Grounded InfErence), an evidence-grounded pipeline that extracts first-person and symptom-specific text spans, selects a representative subset using a Facility Location-inspired objective, and performs Evidence-Gated Inference over the selected evidence. We evaluate evidence retrieval on BDI-Sen and evidence-conditioned questionnaire inference on eRisk 2019-2021. Compared with state-of-the-art retrieval approaches, SREE achieves relative improvements of 70% in post-level precision and 59% in F1, while retaining at least 30% fewer posts. On multi-symptom sentences, it also outperforms dense retrieval in precision and F1 even with twice the retrieval budget. Across all three eRisk collections, SREE achieves the highest exact item-level agreement while remaining competitive on ordinal and aggregate metrics. These results highlight the importance of localizing attributable evidence and controlling its composition before inferring depression questionnaire responses.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.12900 [cs.CL] |
| (or arXiv:2611.12900v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12900 arXiv-issued DOI via DataCite |
Submission history
From: Sumanth Manduru [view email]
[v1]
Wed, 14 Oct 2026 05:29:13 UTC (1,595 KB)
来源：arXiv cs.CL · arxiv.org