Improvement of metaphor understanding via a cognitive linguistic model based on hierarchical classification and artificial intelligence SVM - Dongmei Zhu (Scientific Reports)
Abstract This study aims to enhance computers’ ability to understand and generate metaphors, offering a novel perspective and technical approach in the field of natural language processing. It proposes a metaphor recognition algorithm that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM). First, the text is transformed into numerical features using a pre-trained word embedding model. Then, local contextual features are extracted through a multi-layer CNN. These features are subsequently input into the SVM for classification, enabling optimal metaphor recognition. In English verb metaphor recognition tasks, the model—when combined with the SVM classifier—achieves an accuracy of 85%, an F1 score of 85.5%, and a recall of 86%. In Chinese metaphor recognition experiments, the integration of the SVM classifier significantly improves performance, yielding an F1 score of 81.5%, an accuracy of 81%, and a recall of 82%. In conclusion, the proposed model effectively integrates the CNN’s powerful feature extraction capabilities with the SVM’s superior classification performance. Additionally, it incorporates part-of-speech features to enhance semantic analysis. This integrated approach enables more accurate identification of complex textual semantics, particularly in interpreting metaphorical language that requires deeper understanding.