Improvement of metaphor understanding via a cognitive linguistic model based on hierarchical classification and artificial intelligence SVM.

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.