用三模态图提升主题模型稳定性与精度，解决传统方法的模糊和偏倚问题。
TriTopic: Tri-Modal Graph-Based Topic Modeling with Iterative Refinement and Archetypes
- 融合语义、词频和元数据构建三模态图，抑制噪声并克服高维困境
- 迭代优化使主题嵌入更清晰，平均NMI达0.575，优于BERTopic等主流方法
- 以边界样本定义主题原型，避免中心偏差，适合需要可解释性的研究者
主题建模从大规模文本中提取潜在主题，但主流方法如BERTopic存在随机不稳定性、词汇精度损失（'嵌入模糊'）以及单一数据视角依赖等关键缺陷。本文提出TriTopic框架，通过融合语义嵌入、TF-IDF和元数据的三模态图解决这些问题。核心创新包括：基于互近邻和共享最近邻的混合图构建，消除噪声并缓解维度灾难；共识Leiden聚类实现可复现、稳定的划分；迭代精炼通过动态质心拉拽增强嵌入清晰度。此外，用边界案例定义的原型替代‘平均文档’，提升主题代表性。在20 Newsgroups、BBC News、AG News和Arxiv四个数据集上的实验表明，TriTopic在所有数据集上均达到最高NMI（平均0.575），显著优于BERTopic（0.513）、NMF（0.416）和LDA（0.299）；保证100%语料覆盖且无异常点；已开源为PyPI库。
原文摘要 · Abstract (English)
Topic modeling extracts latent themes from large text collections, but leading approaches like BERTopic face critical limitations: stochastic instability, loss of lexical precision ("Embedding Blur"), and reliance on a single data perspective. We present TriTopic, a framework that addresses these weaknesses through a tri-modal graph fusing semantic embeddings, TF-IDF, and metadata. Three core innovations drive its performance: hybrid graph construction via Mutual kNN and Shared Nearest Neighbors to eliminate noise and combat the curse of dimensionality; Consensus Leiden Clustering for reproducible, stable partitions; and Iterative Refinement that sharpens embeddings through dynamic centroid-pulling. TriTopic also replaces the "average document" concept with archetype-based topic representations defined by boundary cases rather than centers alone. In benchmarks across 20 Newsgroups, BBC News, AG News, and Arxiv, TriTopic achieves the highest NMI on every dataset (mean NMI 0.575 vs. 0.513 for BERTopic, 0.416 for NMF, 0.299 for LDA), guarantees 100% corpus coverage with 0% outliers, and is available as an open-source PyPI library.
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