arXiv cs.CL· Bo Wang, Yuxuan Zhang, Yueqin Hu, Hanchao Hou, Kaiping Peng, Shiguang Ni·· 12 小时前AI 评分27
无需作答的心理量表精简前端：用主题建模发现量表语义结构
Discovering Semantic Structures in Psychological Scales: A Response-Free Front-End to Efficient Simplification
AI 导读
研究者提出一个基于主题建模的免作答框架，用上下文句嵌入编码量表题目、密度聚类自动确定语义主题数量，再按类项加权提取关键词并层次合并，实现量表精简。在 DASS、IPIP、EPOCH 三个量表上，语义主题常形成与既有构念一致的因素式分组，代表性题目使题量平均减少 60.5%，同时保持可接受的心理测量学性质与因素相关模式。团队还提供了支持一键语义分析与结构化精简的可视化工具。
正文 · 原文
Abstract:Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to uncover latent constructs and optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data availability and cross-cultural comparability. Recent advances in natural language processing suggest that the wording of scale items contains construct-relevant semantic organization, offering a complementary response-free perspective. We introduce a topic-modeling framework that operationalizes item-level semantic structure for systematic scale simplification. Items are encoded using contextual sentence embeddings and grouped through density-based clustering to identify semantic topics without predefining their number. Within each cluster, class-based term weighting identifies representative keywords that summarize each topic and support an interpretable semantic pre-structure of the item pool. Semantically related clusters are hierarchically merged, and representative items are selected using structure-aware membership criteria within an integrated reduction pipeline. We evaluated the framework on three widely used instruments: DASS, IPIP, and EPOCH, covering negative affect, personality traits, and well-being. Results showed that the identified semantic topics often formed factor-like groupings aligned with established constructs. Representative items selected through semantic membership probabilities reduced item counts by 60.5% on average while maintaining acceptable psychometric properties and preserving factor correlation patterns. Our framework formalizes semantic topic structure as an inspectable front-end for scale construction and simplification. To facilitate adoption, we provide a visualization-supported tool enabling one-click semantic analysis and structured simplification.
| Comments: | 84 pages, 20 figures; minor revisions |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.12575 [cs.CL] |
| (or arXiv:2602.12575v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.12575 arXiv-issued DOI via DataCite |
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| Journal reference: | Behav Res 58, 338 (2026) |
| Related DOI: | https://doi.org/10.3758/s13428-026-03193-9
DOI(s) linking to related resources |
Submission history
From: Bo Wang [view email]
[v1]
Fri, 13 Feb 2026 03:37:15 UTC (1,124 KB)
[v2]
Sun, 8 Mar 2026 13:01:17 UTC (2,305 KB)
[v3]
Wed, 4 Nov 2026 09:20:29 UTC (1,244 KB)
来源：arXiv cs.CL · arxiv.org