textual-data

该论文提出SHIP（Structured and Hierarchical Insight with Privacy），将差分隐私洞察形式化为可复用的查询回答表示，通过自动构建层级混合分类法并发布DP合成表，实现隐私保护下的文本数据分析，性能优于现有私有洞察基线。

This paper introduces GRaCE, an unsupervised framework for generating interpretable graph embeddings using rank-based measures, which outperforms existing methods in retrieval, classification, and clustering tasks on textual and image data.

This paper proposes LMT, a Bayesian causal discovery framework that combines LLM-extracted semantic signals from textual alarm records with timestamp-based statistical evidence to infer causal graphs in manufacturing systems.