[Objective] This paper presents a new method to recognize metaphor, from the Chinese and English datasets. [Methods] First, we mapped the experimental dataset to vector space, which was also input to a convolutional neural network along with the property and keyword features. Then, we extracted the needed features with the help of convolutional and pooled layers, as well as classified them using SVM. Finally, we combined the Max-Pooling and Mean-Pooling to improve the extracted features’ accuracy. [Results] Compared with the traditional models, our method increased the accuracy of extracted features from the corpus of English verb-object, English adjective-noun and Chinese metaphor by 4.12%, 0.84% and 4.50% respectively. [Limitations] The Chinese word segmentation affects the training of word vector model. We need to add more layers to the convolutional neural networks. [Conclusions] The proposed method could effectively identify metaphor from Chinese and English corpus.
bright smile bushy eyebrows cautious smile dark history deep faith desolate beauty economic battle fading memory faint impression
blue fence blinding light biting dog bright sun bright light burning tree burning arm dark face dirty hands
TSV-TRAIN中的形容词-名词短语
实验
准确率
CNN-sentence-eng
81.80%
CNN_SVM -sentence-eng
87.23%
CNN-Word/Pos-eng
86.00%
CNN_SVM -Word/Pos-eng
90.12%
CNN-AN-eng
86.36%
CNN_SVM -AN-eng
87.20%
Rei等[13]
83.00%
英文语料隐喻识别
实验
准确率
CNN-sentence-ch
72.5%
CNN_SVM -sentence-ch
77.00%
中文语料隐喻识别
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