期刊
出版社
IEEE
DOI: 10.1109/ISCID.2018.10144
关键词
text classification; word2vec; convolutional neural network; long short-term memory network; hybrid model
Aiming at the traditional methods of text classification, the dimensions need to be reduced, the features are extracted manually, and the classification accuracy is poor, furthermore, convolutional neural network CNN can only extract local information, cannot better express context information, long short-term memory network LSTM can extract context dependencies, and the classification effect is good, but the training time is long, a text classification algorithm based on hybrid CNN-LSTM hybrid model is proposed. The algorithm uses the Skip-Gram (continuous skip-gram) model and the CBOW (continuous bag-of-words) model in word2vec to represent words as vector, using CNN to extract local features of text, LSTM saves historical information, extracts contextual dependencies of text, and uses the feature vector output by CNN as the input of LSTM, using Softmax classifier for classification. Tests on the Chinese news corpus of Sogou. com show that the algorithm can effectively improve the precision of text classification.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据