CMNIE：中文军事新闻信息抽取基准

中文摘要 AI 辅助

从中文军事新闻中进行结构化抽取可支持情报分析、决策制定和知识库构建。然而，现有资源在该领域的联合信息抽取方面支持有限，尤其是当事件、事件参数、实体和关系需要被统一建模时。我们提出了CMNIE，一个面向中文军事新闻的信息抽取基准。通过将军事领域资源扩展到文档级事件标注之外，CMNIE在统一领域模式（schema）下联合标注了事件触发词、事件参数、命名实体和实体关系。该数据集包含从公开中文军事新闻中收集的13,000个实例，并针对7种事件类型、10种参数角色、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有监督信息抽取模型、零样本大语言模型以及基于微调LLM的抽取方法。实验结果表明，CMNIE仍具有挑战性，尤其是在关系抽取和事件参数跨度精确匹配方面；零样本大语言模型通常能识别相关的语义单元，但无法精确匹配金标准跨度边界。CMNIE为研究中文专业新闻中的模式遵循、精确跨度匹配和联合结构化抽取提供了一个标准化基准。

英文摘要

Structured extraction from Chinese military news supports intelligence analysis, decision-making, and knowledge base construction. However, existing resources provide limited support for joint informa?tion extraction in this domain, especially when events, event arguments, entities, and relations must be modeled together. We present CMNIE, an information extraction benchmark for Chinese military news. Extend?ing military-domain resources beyond document-level event annotations, CMNIE jointly annotates event triggers, event arguments, named enti?ties, and entity relations under a unified domain schema. 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. Experimental results show that CMNIE remains chal?lenging, 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 stan?dardized benchmark for studying schema adherence, exact span match?ing, and joint structured extraction in specialized Chinese news.

发表机构

• Laboratory for Big Data and Decision, National University of Defense Technology（国防科技大学大数据与决策实验室）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

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13 Pages, 3 figures, accpeted by NLPCC 2026