【摘要】 【目的】针对中英文的隐喻数据集,提出一种基于卷积神经网络与SVM分类器的隐喻识别方法。【方法】将实验数据向量化,结合词性特征和关键词特征作为卷积神经网络的输入,通过卷积层和池化层提取特征,应用SVM进行分类。针对卷积神经网络的池化层中特征采样的不完全性,提出将MaxPooling与Mean Pooling组合在一起的改进方法。【结果】相对于直接使用卷积神经网络,利用本文方法进行隐喻识别的准确率在英文动宾语料、英文形容词–名词词组语料和中文隐喻语料分别提高4.12%、0.84%和4.50%。【局限】中文分词不准确,影响词向量模型训练;卷积神经网络的层数过少,影响特征的完整性。【结论】根据中英文数据集上隐喻识别的结果分析,该方法在两个数据集上都取得了良好效果。更多还原

【Abstract】 [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.更多还原