从文本到语境：一种用于新闻利益相关者分类的蕴含方法
From Text to Context: An Entailment Approach for News Stakeholder Classification
AI总结：
针对新闻内容中利益相关者角色自动检测研究不足的问题，本文提出将分类任务转化为自然语言推理任务，结合语境与外部知识提升检测准确率，且模型在零样本场景下有效，可适配多样新闻语境。
摘要：
梳理新闻文章的复杂格局需要理解其中涉及的各类参与者或实体，这些实体被称为新闻利益相关者。这类利益相关者涵盖政策制定者、反对派人士、公民等，在塑造新闻叙事中发挥着关键作用。识别其利益相关者类型（反映其角色、政治立场、社会地位等），对于细致理解新闻内容至关重要。尽管现有研究聚焦于显著实体抽取、报道差异以及通过社交媒体数据分析政治派别，但新闻内容中利益相关者角色的自动检测仍是一个尚未充分探索的领域。本文通过提出一种有效的新闻文章利益相关者类型分类方法填补了这一空白。我们的方法将利益相关者分类问题转化为自然语言推理任务，利用新闻文章的语境信息和外部知识提升利益相关者类型检测的准确率。此外，我们提出的模型在零样本设置下展现出有效性，进一步拓展了其在多样新闻语境中的适用性。
英文摘要：
Navigating the complex landscape of news articles involves understanding the various actors or entities involved, referred to as news stakeholders. These stakeholders, ranging from policymakers to opposition figures, citizens, and more, play pivotal roles in shaping news narratives. Recognizing their stakeholder types, reflecting their roles, political alignments, social standing, and more, is paramount for a nuanced comprehension of news content. Despite existing works focusing on salient entity extraction, coverage variations, and political affiliations through social media data, the automated detection of stakeholder roles within news content remains an underexplored domain. In this paper, we bridge this gap by introducing an effective approach to classify stakeholder types in news articles. Our method involves transforming the stakeholder classification problem into a natural language inference task, utilizing contextual information from news articles and external knowledge to enhance the accuracy of stakeholder type detection. Moreover, our proposed model showcases efficacy in zero-shot settings, further extending its applicability to diverse news contexts.
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。
