Data Augmentation and Gloss-Based Siamese Network for Metaphor Recognition

Metaphor recognition plays a key role in natural language understanding and semantic analysis. This paper introduces a metaphor recognition model called EGSNet (Enhanced Gloss Siamese Network). Previous research has shown that the gloss of metaphor words contributes to their comprehension in metaphor recognition. To leverage this information, this paper incorporates gloss annotations of metaphor words into the metaphor recognition model. Mining the deep semantic information of glossed metaphorical words, and combining MIP linguistic rule to use the annotation information of metaphorical words can better perceive the semantic conflicts between the contextual meaning of the target word and its basic meaning, thereby improving the ability to recognize metaphors. Furthermore, by employing data augmentation techniques to reveal the true meaning of target words in contextual environments and combining gloss information, the EGSNet model can effectively capture subtle semantic differences in sentences as well as the linguistic characteristics of metaphors, thereby enhancing metaphor recognition performance. Experiments indicate that the EGSNet model achieves more accurate metaphor recognition results and makes a significant contribution to the field of metaphor recognition.