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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 77-83    DOI: 10.11925/infotech.2096-3467.2018.0114
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Recognizing Metaphor with Convolution Neural Network and SVM
Xiaoxi Huang,Hanyu Li(),Rongbo Wang,Xiaohua Wang,Zhiqun Chen
Institute of Cognitive and Intelligent Computing, Hangzhou Dianzi University, Hangzhou 310018, China
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[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.

Key wordsMetaphor Recognition      Convolution Neural Network      Support Vector Machines      Feature Extraction     
Received: 29 January 2018      Published: 12 November 2018

Cite this article:

Xiaoxi Huang,Hanyu Li,Rongbo Wang,Xiaohua Wang,Zhiqun Chen. Recognizing Metaphor with Convolution Neural Network and SVM. Data Analysis and Knowledge Discovery, 2018, 2(10): 77-83.

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Verb Noun Class Relation
See development
Live dream
Envy eat
Break window
Boy cry
Paint dry
Metaphorical Literal
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
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%
实验 准确率
CNN-sentence-ch 72.5%
CNN_SVM -sentence-ch 77.00%
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