[Submitted on 9 Sep 2026]
Title:CMNIE: An Information Extraction Benchmark for Chinese Military News
View a PDF of the paper titled CMNIE: An Information Extraction Benchmark for Chinese Military News, by Yan Yu and 5 other authors
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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.
Comments: 13 Pages, 3 figures, accpeted by NLPCC 2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.10722 [cs.CL]
(or arXiv:2609.10722v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.10722
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mengna Zhu [view email] [v1] Wed, 9 Sep 2026 18:16:08 UTC (201 KB)
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