AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[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 View PDF HTML (experimental) 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) Full-text links: Access Paper: View a PDF of the paper titled CMNIE: An Information Extraction Benchmark for Chinese Military News, by Yan Yu and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)