Extracting structured event knowledge, including event triggers and
corresponding arguments, from military texts is fundamental to many
applications, such as intelligence analysis and decision assistance. However,
event extraction in the military field faces the data scarcity problem, which
impedes the research of event extraction models in this domain. To alleviate
this problem, we propose CMNEE, a large-scale, document-level open-source
Chinese Military News Event Extraction dataset. It contains 17,000 documents
and 29,223 events, which are all manually annotated based on a pre-defined
schema for the military domain including 8 event types and 11 argument role
types. We designed a two-stage, multi-turns annotation strategy to ensure the
quality of CMNEE and reproduced several state-of-the-art event extraction
models with a systematic evaluation. The experimental results on CMNEE fall
shorter than those on other domain datasets obviously, which demonstrates that
event extraction for military domain poses unique challenges and requires
further research efforts. Our code and data can be obtained from
this https URL

提取军事文本中的结构化事件知识，包括事件触发器和相应的参数，对于许多应用非常重要，如情报分析和决策辅助。本研究提出了 CMNEE，一个大规模的、以文件级别为单位的开源中国军事新闻事件提取数据集，包含 17,000 个文档和 29,223 个事件，这些事件均按照事先定义的军事领域模式进行手动注释，包括 8 种事件类型和 11 种参数角色类型。与其他领域数据集相比，对 CMNEE 的实验结果明显不理想，这表明军事领域的事件提取面临独特挑战，需要进一步的研究努力。可从此 https URL 获取我们的代码和数据。