面向多标签文本分类的深度主题特征提取

针对单标签特征提取方法不能有效解决多标签文本分类的问题,文中提出融合主题模型(LDA)与长短时记忆网络(LSTM)的双通道深度主题特征提取模型(DTFEM).LDA与LSTM分别作为两个通道,通过LDA为文本的全局特征建模,利用LSTM为文本的局部特征建模,使模型能同时表达文本的全局特征和局部特征,实现有监督学习与无监督学习的有效结合,得到文本不同层次的特征提取.实验表明,相比文本特征提取模型,文中模型在多标签分类结果上的多项指标均有明显提升.

Feature Extraction of Deep Topic Model for Multi-label Text Classification

Traditional single-label feature extraction methods cannot effectively solve the problem of multi-label text classification. Aiming at this problem, a dual model of latent dirichlet allocation(LDA) and long short-term memory(LSTM), deep topic feature extraction model(DTFEM), is proposed in this paper. LDA and LSTM are employed as two channels, respectively. LDA is used to model global features of the text, and LSTM is used to model local features of the text. DTFEM can express the global and local features of the text simultaneously and combine supervised learning and unsupervised learning effectively to realize the feature extraction of different levels of text. Experimental results show that DTFEM is superior to other traditional text feature extraction models and obviously improves the indicators of multi-label text classification tasks.