Multi-label text classification model based on semantic embedding

查看全文 作　　者：Yan [1]Danfeng;Ke [1]Nan;Gu [2]Chao;Cui [1]Jianfei;Ding [1]Yiqi

机构地区：[1]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications;[2]Electric Power Research Institute Shandong Electric Power Company

出　　处：《The Journal of China Universities of Posts and Telecommunications》索引2019年第26卷第1期,共10页

摘　　要：Text classification means to assign a document to one or more classes or categories according to content. Text classification provides convenience for users to obtain data. Because of the polysemy of text data, multi-label classification can handle text data more comprehensively. Multi-label text classification become the key problem in the data mining. To improve the performances of multi-label text classification, semantic analysis is embedded into the classification model to complete label correlation analysis, and the structure, objective function and optimization strategy of this model is designed. Then, the convolution neural network(CNN) model based on semantic embedding is introduced. In the end, Zhihu dataset is used for evaluation. The result shows that this model outperforms the related work in terms of recall and area under curve(AUC) metrics. 关键词：MULTI-LABEL TEXT classification CONVOLUTION NEURAL network SEMANTIC analysis

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Multi-label text classification model based on semantic embedding

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耦合文献(337)

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