计算机工程2025，Vol.51Issue(5)：143-153,11.DOI:10.19678/j.issn.1000-3428.0068521
基于全局与局部特征加权融合的隐喻识别模型
Metaphor Recognition Model Based on Weighted Integration of Global and Local Features
摘要
Abstract
This study presents a metaphor recognition model based on the weighted integration of global and local features to address the problem of the location of a metaphor body and the metaphor body being far apart in some texts.This issue makes it difficult for a model to learn the contextual information of texts,and consequently the important information of extracted features remaining unclear.First,a Local Feature Extraction Module(LFEM)is designed to learn contextual information at different distances around words by focusing on local features across different ranges and larger receptive fields.Second,Bidirectional Long Short-Term Memory(BiLSTM)and multi-head attention are used to construct a Global Feature Extraction Module(GFEM)to learn macrosentence-level global features.Finally,a Feature Weighted Fusion Module(FWFM)is designed to perform adaptive dynamic fusion of the two extracted features and obtain more robust features with less noise and more concentrated important information.Experimental results show that compared to the RoBERTa+Transformer+GCN model,the F1 value of the proposed model increases by 1.1,1.2,and 3.2 percentage points on the VUA ALLPOS,TOEFL ALLPOS,and CCL datasets,respectively.The proposed model has higher metaphor recognition accuracy.关键词
隐喻识别/全局特征/局部特征/特征加权/注意力机制/双向长短时记忆Key words
metaphor recognition/global feature/local feature/feature weighting/attention mechanism/Bidirectional Long Short-Term Memory(BiLSTM)分类
计算机与自动化引用本文复制引用
马月坤,马铭佑..基于全局与局部特征加权融合的隐喻识别模型[J].计算机工程,2025,51(5):143-153,11.基金项目
河北省工业智能感知重点实验室资助(SZX2021013). （SZX2021013）
