基于生成式-判别式混合模型的可解释性文档分类

现有可解释性文档分类常忽略对文本信息的深度挖掘,未考虑单词与单词上下文、句子与句子上下文之间的语义关系.为此,文中提出基于生成式-判别式混合模型的可解释性文档分类方法,在文档编码器中引入分层注意力机制,获得富含上下文语义信息的文档表示,生成精确的分类结果及解释性信息,解决现有模型对文本信息挖掘不够充分的问题.在PCMag、Skytrax评论数据集上的实验表明,文中方法在文档分类上性能较优,生成较准确的解释性信息,提升方法的整体性能.

Interpretability Document Classification Based on Generative-Discriminatory Hybrid Model

The deep mining of text information and the semantic relationships between word and word context and between sentence and sentence context are not taken into account in the existing interpretability document classification.Therefore,an interpretable document classification method based on a generative-discriminatory-hybrid model is proposed.The hierarchical attention mechanism is introduced into the document encoder to obtain the document representations rich in contextual semantic information.And thus more accurate classification results and explanatory information are generated,and the problem of insufficient text information mining in the existing models is handled.Experiments on PCMag and Skytrax comment datasets show that the proposed method has better performance in document classification,generates more accurate explanatory information and improves the overall performance of the method.