摘要
【目的】以拟人为无标范畴的辞格代表,探索多维融合的拟人辞格识别策略。【方法】依据依存句法理论,通过认知框架构建拟人辞格生成与理解认知模型,提出一种多特征融合的拟人辞格自动识别方法WPGBA,该方法对修辞文本的词向量、语法向量、词性向量、上下文语义等多维特征分别表征与融合,使用K12阶段语文教材作为实验数据。【结果】通过WPGBA方法完成自动识别模型训练,实验显示在识别实验中准确率为90.40%,召回率为87.58%,F1值为88.65%,相较实验组其他方法准确率最少提升6.27个百分点。【局限】在进行篇章阅读理解、语言能力评测等实际应用时会出现新的复杂句,由于实验数据集规模有限,方法泛化能力受到制约。【结论】从认知角度出发设计的表意特征与上下文语义特征融合策略,对无标范畴中拟人辞格具有较好识别效果。
[Objective]Taking personification as a representative of unmarked rhetorical categories,this study explores a multidimensional fusion recognition strategy,which holds significance for Chinese rhetorical computing.[Methods]Based on dependency syntax theory,we constructed a cognitive model for generating and understanding personification rhetorical figures through a cognitive framework.Then,we proposed a multidimensional feature fusion automatic recognition method for personification(WPGBA).This method represents and integrates multiple features of rhetorical texts,including word vectors,syntax vectors,part-ofspeech vectors,and contextual semantics,using Chinese language textbooks from the K-12 curriculum as experimental data.[Results]We trained the automatic recognition model using the WPGBA method.Experiments showed that the method achieved an accuracy of 90.40%,a recall rate of 87.58%,and an F1 score of 88.65%.Compared to other methods in the experimental group,the accuracy rate was increased by at least 6.27%.[Limitations]New complex sentences may arise in practical applications such as discourse reading comprehension and language proficiency evaluation.Due to the limited scale of the experimental dataset,the generalization ability of the algorithm is restricted.[Conclusions]The integration strategy of expressive and contextual semantic features designed from a cognitive perspective shows good recognition performance for personification rhetorical devices in unmarked categories.
作者
张凯
吕学强
Zhang Kail;Lv Xueqiang(Research Center for Language Intelligence of China,Capital Normal University,Beijing 100048,China;Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing Information Science&Technology University,Beijing 100101,China)
出处
《数据分析与知识发现》
北大核心
2025年第2期81-93,共13页
Data Analysis and Knowledge Discovery
基金
国家语委科研规划项目(项目编号:YB145-56)
网络文化与数字传播北京市重点实验室开放课题(项目编号:22220010001)的研究成果之一。
关键词
认知框架理论
拟人生成与识别模型
修辞计算
辞格识别
Cognitive Framework Theory
Anthropomorphic Generation and Recognition Model
Rhetorical Calculation
Rhetorical Recognition
