【摘要】 在搜索引擎、问答系统中利用深度学习的方法计算问题相似度是NLP领域研究的热点。结合卷积神经网络(CNN)和长短记忆网络(LSTM),提出了递归卷积神经网络(RCNN)问句相似度的计算方法,首先利用双向递归神经网络提取上下文信息,然后采用1D卷积神经网络将词嵌入信息与上下文信息进行融合;再利用全局最大池化提取关键信息来完成问句的语义表示;最后通过匹配层判断问句对的相似度。在Quora Question Pairs数据集上的实验结果表明,该相似度计算方法准确率为83.57%,优于其他方法。更多还原

【Abstract】 Using deep learning to calculate the question similarity in search engine and question answering system is a hotspot in the NLP field. Combining convolutional neural network(CNN) and long-short memory network(LSTM), a recursive convolutional neural network(RCNN) question similarity calculation method is proposed. Firstly, the bidirectional recursive neural network is utilized to extract context information, and then the 1 D Convolutional Neural Network was used to integrate the word embedded information with the context information. Then the global maximum pooling is used to extract the key information to complete the semantic representation of the two questions.Finally, the similarity of the question pair is judged through the matching layer. The experimental results show that, based on the Quora Question Pairs data set, the accuracy of the question similarity calculation method is 83.57%, which is better than other methods.更多还原