用实体关系图增强大模型嵌入，提升文档聚类效果
Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering
- 构建文档-实体关系图，用GCN学习语义关联
- 在实体丰富的文档上，聚类准确率显著优于传统方法
- 适合需要细粒度语义分组的文本分析任务
近年来，以BERT、GPT为代表的大语言模型提供了丰富的上下文嵌入，提升了文本表征能力。然而，现有文档聚类方法常忽视命名实体（NE）间的深层关系及大模型嵌入的潜力。本文提出一种融合命名实体识别（NER）与大模型嵌入的图结构聚类方法：构建以文档为节点、基于实体相似性加权的边的图，并通过图卷积网络（GCN）优化，实现更有效的语义相关文档分组。实验表明，该方法在富含命名实体的文档聚类任务中显著优于传统的共现方法。
原文摘要 · Abstract (English)
Recent advances in machine learning, particularly Large Language Models (LLMs) such as BERT and GPT, provide rich contextual embeddings that improve text representation. However, current document clustering approaches often ignore the deeper relationships between named entities (NEs) and the potential of LLM embeddings. This paper proposes a novel approach that integrates Named Entity Recognition (NER) and LLM embeddings within a graph-based framework for document clustering. The method builds a graph with nodes representing documents and edges weighted by named entity similarity, optimized using a graph-convolutional network (GCN). This ensures a more effective grouping of semantically related documents. Experimental results indicate that our approach outperforms conventional co-occurrence-based methods in clustering, notably for documents rich in named entities.
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