Metaphor Recognition and Analysis via Data Augmentation

Metaphoric expression is widespread and frequently used to convey emotions. When it comes to metaphor recognition and analysis, there are still not enough samples for these tasks. In this study, we target on recognizing verb metaphors and analyzing their emotions via data augmentation. To this end, we firstly propose a sentence reconstruction method to prune the dependency parsing tree, and thus alleviates the disturbances caused by the noise information. Then, the data augmentation strategies are proposed based on Seq2Seq model and the reconstructed sentence, which generate sufficient candidate samples after an effective quality evaluation. Finally, a proposed model is trained with the extended dataset, and it achieves the recognition and emotion analysis for metaphors. Experiments are conducted on Chinese and English metaphor corpus respectively, and results show that our proposed model has the best performance compared with the baseline methods.