将大型语言模型与知识图谱结合以捕捉新闻媒体中的政治观点
Integrating Large Language Models and Knowledge Graphs to Capture Political Viewpoints in News Media
AI总结：
本文提出利用大型语言模型和知识图谱结合的方法，提升新闻媒体中政治观点的识别与分类性能。
摘要：
新闻来源在民主社会中扮演着核心角色，通过特定主题、观点和声音塑造政治和社会 discourse。理解这些动态对于评估媒体景观是否提供平衡和公正的公共辩论叙述至关重要。在先前的工作中，我们介绍了一条流程，给定一个新闻语料库，i) 使用混合的人机方法识别关于特定主题表达的观点范围，ii) 对相关主张进行分类，相对于已识别的观点，定义为一组语义和意识形态上一致的主张（例如，主张移民对英国经济有积极影响的立场）。在本文中，我们通过i) 对大型语言模型（LLMs）进行微调以进行观点分类，以及ii) 用来自Wikidata的有关相关行动者的语义描述丰富主张表示来改进该流程。我们在以英国移民辩论为中心的基准上评估我们的方法，结果表明，尽管两种机制单独都能提高分类性能，但其整合产生了最佳结果，尤其是在使用能够处理长输入的LLMs时。
英文摘要：
News sources play a central role in democratic societies by shaping political and social discourse through specific topics, viewpoints and voices. Understanding these dynamics is essential for assessing whether the media landscape offers a balanced and fair account of public debate. In earlier work, we introduced a pipeline that, given a news corpus, i) uses a hybrid human-machine approach to identify the range of viewpoints expressed about a given topic, and ii) classifies relevant claims with respect to the identified viewpoints, defined as sets of semantically and ideologically congruent claims (e.g., positions arguing that immigration positively impacts the UK economy). In this paper, we improve this pipeline by i) fine-tuning Large Language Models (LLMs) for viewpoint classification and ii) enriching claim representations with semantic descriptions of relevant actors drawn from Wikidata. We evaluate our approach against alternative solutions on a benchmark centred on the UK immigration debate. Results show that while both mechanisms independently improve classification performance, their integration yields the best results, particularly when using LLMs capable of processing long inputs.
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。
