用大模型构建新闻知识库，提升新闻分类效果
Knowledge Bases in Support of Large Language Models for Processing Web News
- 用规则提取器从新闻中抽取关系三元组构建知识库
- 通过图卷积融合隐式知识与结构化信息，提升分类性能
- 适合需要精准理解新闻内容的场景，如媒体分析
大型语言模型（LLMs）在众多应用中备受关注。尽管其在预训练阶段隐式记忆了海量事实知识，但这些知识因缺乏常识推理能力，在下游应用中常难以有效使用。本文提出一个通用框架，利用LLM辅助构建专用于处理网络新闻的知识库。该框架采用基于规则的新闻信息提取器（NewsIE）从新闻中提取关系三元组，形成知识库，并通过BERTGraph对新闻中隐含的事实知识与新闻IE提取的结构化信息进行图卷积融合，以实现新闻类别分类。框架包含两个轻量级组件：1）NewsIE，用于提取每条新闻的结构化信息（关系三元组）；2）BERTGraph，用于将隐式知识与提取的三元组进行图卷积融合。我们在多个新闻相关数据集上评估了该框架在新闻类别分类任务上的表现，实验结果表明其具有良好的性能。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have received considerable interest in wide applications lately. During pre-training via massive datasets, such a model implicitly memorizes the factual knowledge of trained datasets in its hidden parameters. However, knowledge held implicitly in parameters often makes its use by downstream applications ineffective due to the lack of common-sense reasoning. In this article, we introduce a general framework that permits to build knowledge bases with an aid of LLMs, tailored for processing Web news. The framework applies a rule-based News Information Extractor (NewsIE) to news items for extracting their relational tuples, referred to as knowledge bases, which are then graph-convoluted with the implicit knowledge facts of news items obtained by LLMs, for their classification. It involves two lightweight components: 1) NewsIE: for extracting the structural information of every news item, in the form of relational tuples; 2) BERTGraph: for graph convoluting the implicit knowledge facts with relational tuples extracted by NewsIE. We have evaluated our framework under different news-related datasets for news category classification, with promising experimental results.
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