GINopic：基于图同构网络的主题建模
GINopic: Topic Modeling with Graph Isomorphism Network
AI总结：
针对现有主题模型忽视词语依赖关系的问题，本文提出基于图同构网络的 GINopic 框架以建模词语相关性，并在多个基准上通过内外部评估验证其优于现有模型。
摘要：
主题建模是一种广泛用于分析和探索大型文档集合的方法。近期研究已将预训练上下文化语言模型（例如 BERT 嵌入）纳入主题建模。然而，这些方法往往忽视词语之间相互依赖所传递的内在信息价值。在本研究中，我们提出 GINopic，这是一个基于图同构网络的主题建模框架，用于捕捉词语之间的相关性。通过在多个不同的基准数据集上开展内在评估（包括定量评估和定性评估）以及外在评估，我们证明了 GINopic 相比现有主题模型的有效性，并强调了其推动主题建模发展的潜力。
英文摘要：
Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language models, such as BERT embeddings, into topic modeling. However, they often neglect the intrinsic informational value conveyed by mutual dependencies between words. In this study, we introduce GINopic, a topic modeling framework based on graph isomorphism networks to capture the correlation between words. By conducting intrinsic (quantitative as well as qualitative) and extrinsic evaluations on diverse benchmark datasets, we demonstrate the effectiveness of GINopic compared to existing topic models and highlight its potential for advancing topic modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
