无需词典创建自定义事件数据：一套技巧
Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks
AI总结：
本文提出一套基于NLP的技巧，利用主动学习、大语言模型和问答模型高效创建自定义事件数据集，并生成了旨在取代ICEWS的POLECAT数据集。
摘要：
事件数据，即从文本中自动提取的“谁对谁做了什么”的结构化记录，是国际政治学者的重要数据来源。开发新事件数据集的高昂成本，尤其是使用依赖手工构建词典的自动化系统，意味着大多数研究人员依赖于ICEWS等大型现有数据集，而不是开发针对其特定研究问题优化的定制事件数据集。本文描述了一套用于高效生产自定义事件数据的“技巧”，利用自然语言处理（NLP）的最新进展，使研究人员能够快速生成定制化事件数据集。本文介绍了利用主动学习训练事件类别分类器的技术，使用大型语言模型和标准机器学习分类器以及NLP中预训练的“问答”模型来识别文本中的行动者及行动接受者，并将行动者的指代解析到其维基百科条目以进行分类。我们描述了这些技术如何生成了旨在取代ICEWS的新POLECAT全球事件数据集，并举例说明了学者如何快速生成更小的自定义事件数据集。我们发布了实现新技术的示例代码和模型。
英文摘要：
Event data, or structured records of ``who did what to whom'' that are automatically extracted from text, is an important source of data for scholars of international politics. The high cost of developing new event datasets, especially using automated systems that rely on hand-built dictionaries, means that most researchers draw on large, pre-existing datasets such as ICEWS rather than developing tailor-made event datasets optimized for their specific research question. This paper describes a ``bag of tricks'' for efficient, custom event data production, drawing on recent advances in natural language processing (NLP) that allow researchers to rapidly produce customized event datasets. The paper introduces techniques for training an event category classifier with active learning, identifying actors and the recipients of actions in text using large language models and standard machine learning classifiers and pretrained ``question-answering'' models from NLP, and resolving mentions of actors to their Wikipedia article to categorize them. We describe how these techniques produced the new POLECAT global event dataset that is intended to replace ICEWS, along with examples of how scholars can quickly produce smaller, custom event datasets. We publish example code and models to implement our new techniques.
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。
