利用码本知识与NLI和ChatGPT进行零样本政治关系分类
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification
AI总结：
本文研究零样本政治关系分类，提出基于NLI的ZSP模型，通过分解任务并利用码本知识，在无需大量标注的情况下优于词典方法并接近监督模型。
摘要：
是否有可能在不断演化的事件本体中，在没有大量标注的情况下准确分类政治关系？本研究探讨了利用现有标注码本中专家知识的零样本学习方法，并评估了先进的ChatGPT（GPT-3.5/4）和基于自然语言推理（NLI）的模型ZSP的性能。ChatGPT使用码本的标注摘要作为提示，而ZSP将分类任务分解为上下文、事件模式和类别消歧，以细化任务特定的假设。这种分解增强了可解释性、效率以及对模式变化的适应性。实验揭示了ChatGPT的优势和局限性，并关键性地表明ZSP优于基于词典的方法，且与一些监督模型相比具有竞争优势。这些发现肯定了ZSP在验证事件记录和推进本体开发方面的价值。我们的研究强调了利用迁移学习和现有领域专业知识来提高研究效率和可扩展性的有效性。
英文摘要：
Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation codebook, and evaluates the performance of advanced ChatGPT (GPT-3.5/4) and a natural language inference (NLI)-based model called ZSP. ChatGPT uses codebook's labeled summaries as prompts, whereas ZSP breaks down the classification task into context, event mode, and class disambiguation to refine task-specific hypotheses. This decomposition enhances interpretability, efficiency, and adaptability to schema changes. The experiments reveal ChatGPT's strengths and limitations, and crucially show ZSP's outperformance of dictionary-based methods and its competitive edge over some supervised models. These findings affirm the value of ZSP for validating event records and advancing ontology development. Our study underscores the efficacy of leveraging transfer learning and existing domain expertise to enhance research efficiency and scalability.
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。
