摘要
短文本分类是自然语言处理的一个研究热点.为提高文本分类精度和解决文本表示稀疏问题,提出了一种全新的文本表示(N-of-DOC)方法.采用Word2Vec分布式表示一个短语,将其转换成的向量作为卷积神经网络模型的输入,经过卷积层和池化层提取高层特征,输出层接分类器得出分类结果.实验结果表明,与传统机器学习(K近邻,支持向量机,逻辑斯特回归,朴素贝叶斯)相比,提出的方法不仅能解决中文文本向量的维数灾难和稀疏问题,而且在分类精度上也比传统方法提高了4.23%.
Short text classification is one of the hotspots of research in natural language processing. A new model of text representation is proposed in this study (N-of-DOC), and in order to solve the problem of sparse representation in Chinese, the Word2Vec distributed representation is used, finally, it is applied to the improved Convolution Neural Network (CNN) model to extract the high level features from the filter layer, the classification model is obtained by connecting the Softmax classifier after the pooling layer. In the experiment, the traditional text representation model and the improved text representation model are used as the input of the original data, respectively. It acts on the model of traditional machine learning (KNN, SVM, logistic regression, naive Bayes) and the improved CNN model. The results show that the proposed method can not only solve the dimension disaster and sparse problem of Chinese text vectors, but also improve the classification accuracy by 4.23% compared with traditional methods.
作者
陈巧红
王磊
孙麒
贾宇波
CHEN Qiao-Hong;WANG Lei;SUN Qi;JIA Yu-Bo(School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China)
出处
《计算机系统应用》
2019年第5期137-142,共6页
Computer Systems & Applications
基金
浙江省自然科学基金(LY17E050028)~~
关键词
卷积神经网络
短文本分类
文本表示
机器学习
深度学习
Convolution Neural Network (CNN)
short text classification
text representation
machine learning
deep learning
