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基于双通道词向量的ACRNN文本分类.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . Apr2021, Vol. 38 Issue 4, p1033-1037. 5p. - Publication Year :
- 2021
-
Abstract
- Common models of text classification are mostly constructed with recurrent neural network and convolutio-nal neural network in a stacked way. Although this stacked structure can extract more high-dimensional and deeper semantic information, a part of the effective feature information is also dropped when different structures are connected . In order to solve the above problem, this paper proposed a classification model based on dual-channel word vectors, and the model used a shallower structure with attention-mechanism-based Bi-LS TM and CNN to extract features of text representation effectively. In addition, this paper presented a new method to characterize text into two forms, forward and backward, and used CNN to extract feature information of the text . By conducting classification experiments on two different five -classification datasets and comparing with a variety of benchmark models, it verifies that the model is effective and the results show that this model is superior to the other models with stacked structure. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 38
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 149740201
- Full Text :
- https://doi.org/10.19734/j.issn.1001-3695.2020.05.0127