@inproceedings{zhangxiaoyi-etal-2025-ccl25,
    title = "{CCL}25-Eval任务4系统报告:基于叙实性分类和语境特征的大语言模型叙实性推理",
    author = "Zhangxiaoyi, Zhangxiaoyi  and
      鲁嘉琪, 鲁嘉琪  and
      Da, Zhang  and
      Chen, Xiaoyu  and
      卢达威, 卢达威",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.11/",
    pages = "96--104",
    abstract = "``叙实性推理是机器理解文本隐含事实的关键能力之一,核心在于结合动词的语义判断动词宾语命题的真值。本研究基于首届中文叙实性推理评测任务4(FIE2025)开展叙实性推理研究,经过前期对不同模型的测验和比对,选择了Deepseek-R1模型为基座模型。提示语的总体撰写思路是:首先将动词叙实性进行分类,从传统的三分法扩展至五分法(叙实、弱叙实、反叙实、非叙实、半叙实),同时,对自然语料与人造语料进行差异化处理,再针对部分语义复杂的动词编写更加细致的判断规则。最终结果显示,自然语料的正确率达到0.9155,人造语料的正确率为0.9541,总正确率达到0.9261。''"
}