CMNIE: An Information Extraction Benchmark for Chinese Military News

CMNIE：中文军事新闻信息抽取基准

Abstract: Structured extraction from Chinese military news supports intelligence analysis, decision-making, and knowledge base construction. However, existing resources provide limited support for joint information extraction in this domain, especially when events, event arguments, entities, and relations must be modeled together.

摘要： 从中文军事新闻中进行结构化抽取，能够为情报分析、决策制定和知识库构建提供支持。然而，现有的资源对该领域的联合信息抽取支持有限，特别是在需要同时对事件、事件论元、实体和关系进行建模时。

We present CMNIE, an information extraction benchmark for Chinese military news. Extending military-domain resources beyond document-level event annotations, CMNIE jointly annotates event triggers, event arguments, named entities, and entity relations under a unified domain schema.

我们提出了 CMNIE，这是一个针对中文军事新闻的信息抽取基准。CMNIE 将军事领域的资源从文档级事件标注扩展开来，在统一的领域模式下，联合标注了事件触发词、事件论元、命名实体以及实体关系。

The dataset contains 13,000 instances collected from public Chinese military news, with manual annotations for 7 event types, 10 argument roles, 7 entity types, and 8 relation types. We evaluate supervised IE models, zero-shot large language models, and fine-tuned LLM-based extraction methods on a shared test set.

该数据集包含从公开中文军事新闻中收集的 13,000 个实例，并对 7 种事件类型、10 种论元角色、7 种实体类型和 8 种关系类型进行了人工标注。我们在一个共享测试集上评估了监督式信息抽取模型、零样本大语言模型以及经过微调的基于大语言模型的抽取方法。

Experimental results show that CMNIE remains challenging, especially for relation extraction and exact matching of event-argument spans; zero-shot LLMs often identify relevant semantic units but fail to match gold span boundaries exactly. CMNIE provides a standardized benchmark for studying schema adherence, exact span matching, and joint structured extraction in specialized Chinese news.

实验结果表明，CMNIE 仍然具有挑战性，特别是在关系抽取和事件论元跨度的精确匹配方面；零样本大语言模型通常能识别相关的语义单元，但往往无法精确匹配标准（Gold）跨度边界。CMNIE 为研究专业中文新闻中的模式遵循、精确跨度匹配和联合结构化抽取提供了一个标准化基准。