[Objective] This paper proposes a method to recognize Chinese and English metaphors with word vector combination and recurrent neural network (RNN), aiming to identify the ubiquitous metaphors from natural languages. [Methods] First, we mapped texts to the word vectors as inputs of the neural network with the help of word-embedding combination algorithm. Then, we used the RNN as encoder, and took the attention mechanism and the pooling technique as feature extractor. Finally, we utilized Softmax to calculate the probability of the text was a metaphor. [Results] The accuracy and F1 of the proposed method with English texts improved by 11.8% and 6.3%, compared with traditional method based on vanilla word embedding. For Chinese tasks, the accuracy and F1 of the proposed method also improved by 8.9% and 7.8%. [Limitations] Due to the long-distance dependence issue, our method could not effectively recognize metaphors in long texts with complex sentences. [Conclusions] The proposed model signifcantly improves the neural network’s ability to recognize metaphors.
苏传东,黄孝喜,王荣波,谌志群,毛君钰,朱嘉莹,潘宇豪. 基于词嵌入融合和循环神经网络的中英文隐喻识别*[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
Su Chuandong,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Mao Junyu,Zhu Jiaying,Pan Yuhao. Identifying Chinese / English Metaphors with Word Embedding and Recurrent Neural Network. Data Analysis and Knowledge Discovery, 2020, 4(4): 91-99.
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