摘要： 以词向量为输入的单通道卷积神经网络无法充分利用文本的特征信息，并且不能准确识别中文文本的多义词。针对上述问题，建立一种细粒度的多通道卷积神经网络模型。采用word2vec进行词向量的预训练，利用3个不同的通道做卷积运算，分别为原始词向量、词向量与词性表示相结合的词性对向量以及细粒度的字向量。通过词性标注进行词义消歧，利用细粒度的字向量发现深层次的语义信息。在此基础上，设置不同尺寸的卷积核以学习句子内部更高层次抽象的特征。仿真结果表明，该模型较传统卷积神经网络模型在情感分类的准确率和F1值上性能均有明显提升。

关键词: 卷积神经网络, 词向量, 词性对向量, 情感分类, 文本情感分析

Abstract: It is hard for single-channel Convolutional Neural Network(CNN) that take word vectors as the input to fully utilize the information of text features and accurately recognize polysemantic of Chinese texts.To address the problem,this paper proposes a fine-grained multi-channel CNN model.It uses word2vec for pre-training of word vectors.Three different channels are used for convolution operations,which are original word vector,part-of-speech pair word vector that combines word vector with part of speech representation,and fine-grained character vector.Part of speech is labeled to disambiguate words,and character vectors are used to discover hidden semantic information.On this basis,convolutional kernels of different sizes are set to learn the features of higher-level abstractions in sentences.Simulation results show that compared with traditional CNN models,this model can significantly improve the accuracy and F1 value of sentiment classification.

Key words: Convolutional Neural Network(CNN), word vector, part of speech vector, sentiment classification, sentiment analysis of texts

中图分类号: 

• TP391.1