{"title":"Document Classification Based on semantic and Improved Convolutional Neural Network","authors":"Rong Li, Wei-Bai Zhou, Wei Liu","doi":"10.1145/3417188.3417196","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy of text classification, we present a new convolution neural network model combining keyword and word-meaning transformation. We first preprocess the text and break words, and use sense labeling for semantic keywords and word-meaning transformation. and we divide the texts into two parts---word and word-meaning. Next, we use embedding layer to transform the word and word-meaning into corresponding word embedding. Then, we use improved convoluted neural network to train the model and extract higher-order features of text type data, and use multi-layer perceptron and SoftMax layer to classify the texts to predict the category of each text. Experimental results show that our document classification algorithm can get a high accuracy and the effect of classification of news topic detection gets well.","PeriodicalId":373913,"journal":{"name":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3417188.3417196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In order to improve the accuracy of text classification, we present a new convolution neural network model combining keyword and word-meaning transformation. We first preprocess the text and break words, and use sense labeling for semantic keywords and word-meaning transformation. and we divide the texts into two parts---word and word-meaning. Next, we use embedding layer to transform the word and word-meaning into corresponding word embedding. Then, we use improved convoluted neural network to train the model and extract higher-order features of text type data, and use multi-layer perceptron and SoftMax layer to classify the texts to predict the category of each text. Experimental results show that our document classification algorithm can get a high accuracy and the effect of classification of news topic detection gets well.