Problem
Research questions and friction points this paper is trying to address.
information extraction
Chinese military news
joint structured extraction
event arguments
entity relations
CMNIE: An Information Extraction Benchmark for Chinese Military News
🤖 AI Summary
为解决中文军事新闻信息抽取问题，提出CMNIE基准，联合标注事件触发词、事件参数、命名实体和实体关系，评估多种模型性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
information extraction
joint annotation
Chinese military news
unified domain schema
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