构建非洲之角冲突事件数据集，助力低资源场景下冲突分析
CEHA: A Dataset of Conflict Events in the Horn of Africa
- 基于新闻文本构建细粒度冲突事件标注数据集
- 涵盖500条事件描述，聚焦冲突根源与区域特征
- 适合人道主义、和平与发展领域研究者使用
自然语言处理在理解暴力冲突动态与成因方面具有重要作用。尽管已有多种冲突事件数据集，但现有标签未能覆盖非洲之角等地区的关键细粒度冲突类型。本文提出新的基准数据集——非洲之角冲突事件（CEHA），包含500条英文事件描述，采用强调冲突原因的细粒度事件类型定义。该数据集依据人道-和平-发展协同体利益相关方需求，对关键冲突风险类型进行分类。我们在此基础上开展两项任务的实验：事件相关性分类与事件类型分类。基线模型表明这些任务具有挑战性，且本数据集在训练数据有限的低资源环境下具备良好的模型评估价值。
原文摘要 · Abstract (English)
Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict. Despite the availability of datasets categorizing various conflict events, the existing labels often do not cover all of the fine-grained violent conflict event types relevant to areas like the Horn of Africa. In this paper, we introduce a new benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and propose a new task for identifying violent conflict events using online resources with this dataset. The dataset consists of 500 English event descriptions regarding conflict events in the Horn of Africa region with fine-grained event-type definitions that emphasize the cause of the conflict. This dataset categorizes the key types of conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. Additionally, we conduct extensive experiments on two tasks supported by this dataset: Event-relevance Classification and Event-type Classification. Our baseline models demonstrate the challenging nature of these tasks and the usefulness of our dataset for model evaluations in low-resource settings with limited number of training data.
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