[Objective] This paper proposes a metaphor identification model based on graph convolutional neural network and Transformer, aiming to effectively find metaphor expressions with multiple words. [Methods] We used the graph convolutional neural network to extract the structure information from the syntactic dependency tree. Then, we combined the structure with deep semantic representation by the Transformer. Finally, we calculated the probability of metaphorical expression for the target words through SoftMax. [Results] Compared with the existing algorithms, the F1 values of our model increased by 1.9% and 1.7% on UVA VERB and UVA ALL POS datasets. The F1 values were also improved by 1.1% and 1.9% on TOEFL VERB and TOEFL ALL POS. The F1 value increased by 1.2% on the Chinese data CCL. [Limitations] If there is ambiguity or ambiguous referential information in the sentence, our model will not effectively identify the metaphor expressions. [Conclusions] Graph convolutional network and syntactic dependency tree can enrich the semantics of target words, which improves the recognition of single and multi-word metaphors.
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