Metaphor Recognition Method based on Graph Neural Network

Abstract

This Metaphor is a very common language phenomenon. Human language often uses metaphor to express emotion, and metaphor recognition is also an important research content in the field of NLP. Official documents are a serious style and do not usually use rhetorical sentences. This paper aims to identify rhetorical metaphorical sentences in official documents. The use of metaphors in metaphorical sentences depends on the context. Based on this linguistic feature, this paper proposes a BertGAT model, which uses Bert to extract semantic features of sentences and transform the dependency relationship between Chinese text and sentences into connected graphs. Finally, the graph attention neural network is used to learn semantic features and syntactic structure information to complete sentence metaphor recognition. The proposed model is tested on the constructed domain dataset and the sentiment public dataset respectively. Experimental results show that the method proposed in this paper can effectively improve the recognition ability of metaphorical emotional sentences.